Early language development of children at familial risk of dyslexia: Speech perception and production

Available online at www.sciencedirect.com

Journal of Communication Disorders 42 (2009) 180–194

Early language development of children at familial risk of dyslexia: Speech perception and production Ellen Gerrits a,*, Elise de Bree b,1 a

Maastricht University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands b Utrecht University, Utrecht Institute of Linguistics OTS, Janskerkhof 13, 3512 BL Utrecht, The Netherlands Received 26 September 2008; accepted 31 October 2008

Abstract Speech perception and speech production were examined in 3-year-old Dutch children at familial risk of developing dyslexia. Their performance in speech sound categorisation and their production of words was compared to that of age-matched children with specific language impairment (SLI) and typically developing controls. We found that speech perception and production performance of children with SLI and children at familial risk of dyslexia was poorer than that of controls. The results of the at-risk and SLI-group were highly similar. Analysis of the individual data revealed that both groups contained subgroups with good and poorly performing children. Furthermore, their impaired expressive phonology seemed to be related to a deficit in speech perception. The findings indicate that both dyslexia and SLI can be explained by a multi-risk model which includes cognitive processes as well as genetic factors. Learning outcomes: As a result of reading this paper the reader will be able to (1) learn about the relationship between language and literacy; (2) recognise that dyslexia and specific language impairment may show similar areas of language difficulties, and (3) understand that both disorders can be interpreted within a multirisk model, including cognitive processes as well as genetic factors. # 2008 Elsevier Inc. All rights reserved.

1. Introduction Developmental dyslexia is a language-based disorder characterised by a failure in reading and spelling despite conventional instruction, adequate intelligence, visual perception and socio-cultural opportunity. Although there is not yet consensus about the cause(s) of dyslexia, the most widely accepted cognitive explanation is that it stems from an underlying phonological processing deficit (e.g. Miles & Miles, 2001; Ramus et al., 2003). The Phonological Deficit Hypothesis refers to a wide variety of phonological problems of dyslexic adults and children. The aim of the current paper is to gather evidence for an early language deficit in 3-year-old children with at least one dyslexic parent by assessing their speech perception and speech production. The at-risk children’s speech abilities are compared to those of age-matched peers, as well as children with specific language impairment (SLI). This comparison allows a further

* Corresponding author. Tel.: +31 43 3874594; fax: +31 43 3875580. E-mail addresses: [email protected] (E. Gerrits), [email protected] (E. de Bree). 1 Tel.: +31 30 2536052; fax: +31 30 2536000. 0021-9924/$ – see front matter # 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jcomdis.2008.10.004

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

181

understanding of the nature and range of linguistic difficulties that the at-risk children exhibit. Inclusion of children with SLI might be said to provide a ‘benchmark’ of non-typical language acquisition. The comparison between the atrisk and SLI children also adds to the current discussion in the literature that dyslexia and SLI are closely related (Bishop & Snowling, 2004). Previous studies have tended to find an interaction between speech perception and production abilities in children at risk of dyslexia (Carroll & Snowling, 2004) and children with a speech (and language) impairment (Nathan, Stackhouse, Goulandris, & Snowling, 2004), but not all of them have (Scarborough, 1990). This discrepancy might be caused by the different perception tasks used, such as mispronunciation detection, ABX discrimination, and word recognition (the production tasks were highly similar, all measuring percentage consonants correct). We assessed speech perception through a categorical speech perception task. Speech production involved both the percentage of phonemes correct as well as an analysis of the children’s use of phonological simplification processes. We expected that the at-risk children would perform more poorly on the speech perception and production tasks than the controls, but not as poorly as the SLI children. The in-between performance of the at-risk children is predicted by the familial risk: children born in ‘dyslexic families’, have been estimated to have a 40–60% chance of becoming dyslexic, compared to approximately 4% in the population at large (Grigorenko, 2001). It was tested whether (1) the at-risk children and SLI-children would perform more poorly than the controls on the speech perception and production tasks; (2) part of the at-risk children would perform similarly to the children with SLI; and (3) there was a relationship between impaired speech perception and production. Thus, overall performance of the three experimental groups was tested and compared, but individual analyses were also conducted. The study is presented in three parts. The first part (Section 2) will address the speech perception experiment, Section 3 will elaborate on the production task, and the relationship between the perception and production data will be discussed in Section 4. 2. Categorical speech perception The introduction referred to the now strong and highly convergent evidence in support of the Phonological Deficit as underlying cause of dyslexia. Difficulties in phonological awareness and phonological coding, which constitute the primary focus of dyslexia research, are hypothesised to stem from weak phonological representations in long-term memory (e.g. Werker & Tees, 1987). The idea of ‘unstable’ or ‘weak’ phonological representations in dyslexics has developed from (among others) studies showing less consistent categorical perception of speech sounds by dyslexic subjects than normally reading controls. Categorical perception entails that speech sounds are decided upon (by the listener) categorically: acoustic differences between variants within the same phonetic category are not ‘perceived’, whereas acoustic differences between variants that cross a phoneme boundary lead to a different phonemic perception (e.g. a switch from ‘pa’ to ‘ba’). The categorical-perception paradigm demands listeners to categorise syllables stemming from an acoustic continuum of speech sounds. Studies with dyslexic adults and children have shown that their categorisation functions are less steep, thus showing less categorical perception of speech sounds, than that of average readers (e.g. Werker & Tees, 1987). A shallow slope indicates a large range of uncertainty and suggests difficulties in identifying the speech stimuli. It has to be noted however, that in some studies group differences were only marginally significant or non-significant in some stimulus conditions or with some groups of subjects (e.g. Maassen, Groenen, Crul, Assman-Hulsmans, & Gabree¨ls, 2001). These findings have led recent studies to stress the need for individual data analysis, because a group analysis may mask larger speech perception deficits in subgroups of dyslexics (Joanisse, Manis, Keating, & Seidenberg, 2000; Ramus et al., 2003). There is already some evidence of deviant speech perception performance in young children from dyslexic families. Richardson, Leppa¨nen, Leiwo, and Lyytinen (2003) found that speech sound discrimination of 6 months old at-risk infants deviated from age-matched controls. In addition, Carroll and Snowling (2004) have shown that their mispronunciation detection was poorer than that of age-matched controls. However, the speech perception results of Scarborough (1990) and more recently from Boets, Ghesquie`re, Wiering van, and Wouters (2006), seem to contradict the speech perception deficit. Scarborough found good phoneme identification in at-risk children (at 30 and 36 months) compared to controls, even of the subgroup that turned out to be dyslexic at 60 months. In Boets et al. it was shown that 5 years old at familial risk for dyslexia did not differ from controls on a categorical perception task: neither of the child groups presented a strong categorical perception. The at-risk group only presented a marginally significant deficit on the discrimination task.

182

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

Overall, these studies reveal poorer speech perception performance of children with a familial risk of dyslexia. Few studies have directly tested speech perception skills in children with SLI. Even though these studies report poor performance in the speech perception domain (Joanisse & Seidenberg, 1998; Sussman, 1993), the focus has generally been on auditory (non-speech) perception abilities of children with SLI. Since these studies employed non-linguistic stimulus material, their results do not allow conclusions on speech perception per se´ (see review of McArthur & Bishop, 2001). In categorical perception studies, categorisation performance of the SLI group was always less consistent than that of age-matched controls with a less clear phoneme boundary between stimuli in a stop consonant place-of-articulation or voicing continuum (Joanisse & Seidenberg, 1998; Sussman, 1993). Coady, Kluender, and Evans (2005) have argued that poor performance on speech perception tasks may not be due to a speech perception deficit, but rather to a consequence of task demands. However, our results in Alphen et al. (2004) showed that toddlers with SLI perform highly similarly to control peers when they have to categorise vowel stimuli, but not when stop-consonant stimuli are presented. Since the same task was used, this suggests that perception is impaired, especially if perceptually less contrastive speech stimuli have to be processed. Summarising, both dyslexic and language-impaired children have been found to exhibit difficulties with categorical speech perception. Their inability to process speech sounds has led to the hypothesis that the two developmental disorders might simply represent different manifestations of the same underlying speech perception disorder (McArthur & Bishop, 2001; Snowling, Bishop, & Stothard, 2000). One of the goals of this study was to test this hypothesis by comparing categorical perception performance of 3-year-old children with SLI and children at familial risk of dyslexia. Additional goals were to analyse individual perception data of these groups of children and to compare their perceptual behaviour with their speech production skills. To meet these goals, a categorical perception task was conducted in which the children had to categorise stimuli from a continuum between the initial stop consonants in the Dutch words /p&p/ and /k&p/ (‘doll’ and ‘cup’). 2.1. Method 2.1.1. Participants Three groups of Dutch children participated between 3 and 4 years of age: 34 at-risk children, 12 children with SLI, and 23 controls. More children (2 control, 7 at-risk children) were tested, but their data were not analysed because they failed to pass the training criterion, or did not complete the task. The mean age of the at-risk children was 3;10 (years; months), s.d. 3.6 months. They were recruited through calls in newspapers and parent magazines. For children to be included in the at-risk group, at least one parent had to be dyslexic. The parent’s dyslexic status was ascertained through presenting the Dutch dyslexia test battery to the parent (see Alphen et al., 2004). To minimise inclusion of children whose dyslexic parents had a background of language-impairment, a questionnaire was sent out to all parents, ascertaining absence of severe speech and language difficulties in childhood. The SLI-children were 4;3 years old (s.d. 3.5 months) and they were recruited from schools that provide full-time specialised teaching to children with speech and language problems. These children had been classified as specific language-impaired after extensive assessment of their hearing, and verbal and nonverbal abilities. The control children were on average 3;10 years old (s.d. 3.3 months). They were matched in terms of chronological age and were recruited via day-care centres. The parents of the control children reported no oral or written language problems within the family. Non-verbal IQ was assessed at age 5 with a Dutch standardised test (Snijders, Tellegen, & Laros, 1988). The mean IQ’s for the three groups were respectively 108 (at-risk), 99 (SLI), and 114 (controls). Differences on IQ between groups were not significant (F(2, 52) = 3.0, p = .058). 2.1.2. Test and stimulus material Categorical perception was tested with the traditional two-alternative forced choice categorisation task. The stopconsonant continuum ranged from the meaningful word /p&p/ to /k&p/ (resp. ‘doll’ and ‘cup’). These words were natural utterances produced by a male speaker of standard Dutch. Stimuli in the continuum between the two utterances were obtained by interpolation between the relative amplitudes of the spectral envelopes of the words. Stimulus generation resulted in a continuum of 7 stimuli that sounded completely natural and convincingly like utterances of the original speaker.

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

183

2.1.3. Procedure The categorisation task contained a training and a test phase. In the training phase, only the endpoint stimuli were presented to the child. The training criterion was met if the child correctly identified 6 out of 8 training stimuli. The response consisted of pointing at one of two pictures that represented the endpoints of the continuum. In the test phase, each stimulus was presented 6 times in three randomised blocks. If a child lost its concentration during the test phase the experiment was abandoned. The at-risk children were tested in the language acquisition lab of the Utrecht Institute of Linguistics, the children with SLI and the control children were tested at the schools or day-care centres they attended. The stimuli were presented via headphones connected to a speaker and laptop computer. 2.2. Results The categorisation performance of the three groups of children is displayed in Fig. 1. Fig. 1 shows the percentage/ k&p/responses at each stimulus level. First, it can be seen that the slopes of the categorisation functions of the at-risk group and the SLI group are less steep than the function gradient of the control group. This difference between groups was confirmed by statistical analysis. Analysis of variance (repeated measures) was employed to model percent /k&p/ responses as the dependent variable for the seven points on the continuum. All possible interactions between group, stimulus level, and age were considered as potential terms. The final model includes Group (at-risk and SLI), Stimulus, and the interaction between Group and Stimulus as highly statistically significant ( p < .001) parameters for phoneme categorisation. The average slope coefficients for the three groups are 17.85 for the controls and 10.27 and 11.46 for the at-risk and SLI group respectively. Analysis of variance showed that the slope coefficient between groups was significantly different (F(2, 66) = 9.47, p < .001). Pairwise post hoc analysis revealed this was due to the higher slope coefficient of the controls compared to those of the SLI and at-risk children. The shallow slopes in the functions of the at-risk and SLI-children suggest that their phoneme labelling was less consistent, and hence categorical perception was weak or distorted. On the other hand, categorisation of the controls was more consistent resulting in a curve with a steeper slope at the phoneme boundary. 2.2.1. Individual data analysis Analysis of the individual categorical perception results is relevant for two reasons. First, some scholars have questioned the idea that –all– dyslexics have impaired speech perception skills because they found only some cases performed poor on phoneme identification and discrimination (Manis et al., 1997; Ramus et al., 2003). Secondly, the genetic risk of dyslexia is estimated at 40–60% (Grigorenko, 2001) and therefore it could be expected that the at-risk

Fig. 1. Categorisation functions of the three groups of children presented as percent correct /k&p/ (‘cup’) responses for each of the 7 stimuli in the / p&p/ - /k&p/continuum.

184

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

Table 1 The number and percentage of 3-year-old children performing performing at or above the control mean, up to 1s.d. below, and more than 1s.d. below the controls’ mean slope co-efficient (perception) and the mean PCC (production). Perception

At or above control mean Up to 1s.d. below control mean More than 1s.d. below control mean

Production

Control (N = 23)

Risk (N = 34)

SLI (N = 12)

Control (N = 10)

Risk (N = 29)

SLI (N = 10)

70% 13% 17%

15% 29% 56%

42% 8% 50%

60% 20% 20%

31% 14% 55%

10% 0% 90%

group contains two subgroups: those who will and will not develop dyslexia when they are older. By hypothesis it then follows that, if developmental dyslexia is caused by speech perception difficulties, at least a subgroup of the at-risk children will perform poorly on the categorical perception task. To identify poor and good performers, the average slope coefficient of the controls was used as a baseline. This measure, which has been used in several studies to determine statistical group differences, mirrors developmental data. Calculations were made to see how many of the children performed at or above the control mean, up to 1s.d. below, and more than 1s.d. below the mean slope coefficient of the control group. This cut-off based on the mean and standard deviation of the control group is an arbitrary measure, but is helpful in answering the question of at-risk performance. A similar >1s.d. criterion for literacy scores has been used in, for example, Gallagher, Frith, and Snowling (2000). The results of this analysis are displayed in Table 1. The individual data analysis in Table 1 reveals that speech perception performance was poor in about half of the atrisk and the SLI group. It has to be noted that performance of the children in the latter poor subgroups was worse than that of the few poorly performing control children. The mean standard deviations of the poor groups in Table 1 were 1.8s.d. for the controls, at risk 3.1s.d., and 3.4s.d. for the children with SLI. In fact, the slope of the poor SLI subgroup was always below 2s.d. of the overall mean of the controls. This means that the poor performance of the SLI group was worse than that of the other two groups. Surprisingly, almost half of the children with SLI were good performers, indicating that not all language-impaired children have an underlying speech perception deficit. 2.3. Discussion This speech perception experiment revealed that young children at familial risk of dyslexia and children with SLI are less consistent in their categorisation of stop consonants and have a less demarcated phoneme boundary than normally developing children. The results suggest an impaired speech perception of the at-risk children as a group which concurs with previous findings with at-risk infants and toddlers (Carroll & Snowling, 2004; Gerrits, 2003; Richardson et al., 2003). Performance of the at-risk children and children with SLI was very similar, both groups displaying weak categorical perception. The speech perception deficit of the at-risk children in this study is more prominent than found in previous studies with older dyslexic children or adults. This suggests that early speech perception might be more impaired than later speech perception. This is even more interesting when bearing in mind that not all of the present at-risk children are expected to develop dyslexia. Individual analysis showed that about 60% of the at-risk children were classified as poor performers. This coincides with the familial risk (40–60%) that is reported. However, there were fewer at-risk children in the good perception group than would be expected on the basis of their genetic predisposition (only 15% instead of 40%). Thus, 85% of the at-risk children showed weaker categorical perception than the controls. The individual analysis results in a gradual scale skewed towards poor performance. With regard to dyslexia, this implies that poor speech perception might not be a good predictor: more children were affected than expected on the basis of the familial risk. The findings imply that some at-risk children may show weak categorical perception at age 3, but will not develop overt dyslexia at reading age. Relatively poor speech perception alone might not cause dyslexia. This is in line with the multiple risk model of dyslexia by Vellutino, Fletcher, Snowling, and Scanlon (2004). They argue that overt dyslexia might emerge as the level of risk (genetic disposition, environmental factors and cognitive skills) reaches a certain threshold. In addition, poor phonological processing constitutes the risk of reading disability in high-risk children (see Snowling et al., 2000). In line with Vellutino et al., it is hypothesised that an impairment of both speech perception and speech production might signal a higher risk than an impairment in only one of these two domains.

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

185

The individual analysis of the SLI group, established a bimodal distribution of performance: half of the SLI group is classified as poor performers and half as good performers. Thus, a subgroup of the children was severely impaired in their language production without having a categorical perception deficit. This finding suggests that language disorders might only partly be caused by speech perception problems. However, others might argue that the good performing SLI children used to have poor speech perception skills in the past, but have outgrown or compensated this deficit. However, we argue that this is not the case in our group since these children are only 4 years old which means they must heave learned to compensate very fast. In addition, a significant growth of their speech perception skills is expected to affect their speech production skills too. Yet, the children in this study attended special education schools (from 3;0 years onwards, before grade 1 in the Dutch school system). Dutch children with SLI are admitted to these schools when their language disorder is severe and shown to be resistant to usual care, which is speech and language therapy in a private practice. Thus, since they failed to show progress in therapy it seems unlikely that their good speech perception skills used to be poor one or two years ago. To conclude, we argue that the results of the children with SLI show that a language disorder is not necessarily caused by a speech perception deficit. Interestingly, the SLI group seems to contain more good performers than the at-risk group (42% versus 15%). Of course, it should be borne in mind that the present SLI group was small (N = 12) which was a consequence of the inclusion of SLI children that were younger2 than those tested in previous studies. A speech perception disorder might also be one of multiple (cognitive) risk factors that contribute to language impairment. This is proposed in Bishop’s multiple risk model that includes cognitive as well as environmental and familial risk factors to account for developmental language disorders (Bishop, 2003). The present results indicate that children at familial risk of dyslexia and some children with SLI do not perceive speech sounds in a categorical manner. The shape of their categorisation curves implies that they have responded to the continuous acoustic changes in the signal. This indicates that, compared to the controls, these at-risk and SLI children were more sensitive to meaningless acoustic differences (within phoneme category) between speech sounds and less sensitive to meaningful acoustic differences (across phoneme categories). This finding is in line with previous categorisation studies with dyslexics (e.g. Werker & Tees, 1987) and with findings in Serniclaes, Van Heghe, Mousty, Carre´, and Sprenger-Charolles (2004) who showed that children with dyslexia were better in discriminating withincategory stimuli and worse in discrimination across-category stimuli than controls. Our data suggest that a continuous perception model rather than a categorical perception model could capture the children’s perception of the stop-contrast stimuli. The continuous perception implies that these children display a problem with assigning phonemic (language) labels and instead process the stimuli as if they are non-linguistic sounds. If stimuli are processed without their phonetic labels it is expected that listeners will be sensitive to the small differences between the seven stimuli in the continuum. This is exactly what the result of the at-risk children and children with SLI show: a gradual, continuous change in perception from /p&p/ to /k&p/. It will be interesting to see whether this perceptual deficit is related to speech production errors. This will be discussed in the following sections. 3. Speech production Several studies have found that children with dyslexia exhibit residual speech difficulties: they have been found to display word-specific rather than phoneme-specific production errors, especially with multi-syllabic words (e.g. Swan & Goswami, 1997). It has been argued that these word-specific production difficulties are markers of other phonological difficulties, i.e. they reflect the children’s poor phonological representations and processing. Studies conducted into the speech production of children with a familial risk of dyslexia so far have found mixed results. Some studies have found poorer performance of the at-risk children compared to their controls (e.g. Carroll & Snowling, 2004; Elbro, Borstrøm, & Petersen, 1998; Lambrecht Smith, in press; Scarborough, 1990), but less pronounced differences (Gallagher et al., 2000; Locke et al., 1997) have also been found. The studies that found significant group differences have generally concentrated on measuring Percentage of Consonants Correct (PCC), whereas those with a less pronounced difference included qualitative analyses, by looking at the types of errors that 2

More children were tested but at a later point in time turned out to have additional problems, e.g. autistic spectrum disorder, or showed resolved language problems due to intensive speech therapy. These children were excluded from the study.

186

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

occurred. The present study will include both quantitative as well as qualitative analyses to assess whether the two types of analyses yield different results, whether errors are the same or different for the groups of children, and what the potential areas of difficulty are. Investigation in expressive phonology of children with SLI has consistently shown more severe difficulties in their expressive phonology (e.g. Aguilar-Mediavilla, Sanz-Torrent, & Serra-Ravento´s, 2002; Orsolini, Sechi, Maronatoc, Bonvino, & Corcelli, 2001; Roberts, Rescorla, Giroux, & Stevens, 1998). Typically, the phonological production of these children consists of many, and persistent, simplification processes. These processes include difficulties on the word-, syllable-, phoneme- and feature-domain. Generally, the processes that occur signal a delay in development, but unusual production patterns have also been noted (e.g. Stoel-Gammon & Dunn, 1985). In light of these pervasive phonological difficulties in children with SLI, the expectation is that difficulties will be attested in many, if not all, children with SLI. This study assessed the speech production of children at-risk of dyslexia and children with SLI by computing both the PCC and phonological mean length of utterance (PMLU), as well as the percentage occurrence of different simplification strategies. The simplification processes analysed encompassed word-, syllable-, phoneme- and featurelevel. The reason for looking at these processes was to find out whether a difference would occur between segmental realisations and higher-level structures. If this were the case, this would suggest that segmental representations would be specified poorly, but word-and syllable-level representations would not. Such a finding would be in line with assumptions that dyslexia is characterised by a deficit in phonological representations with mainly ‘fuzzy’ segmental boundaries. Secondly, the nature of the potential speech problems was of interest. It could, for example, be the case that the at-risk and SLI children resort more to unlikely phonological simplification processes, such as cluster or onset omission. Summarising, the objective of the present study was to gain insight into the speech production and phonological processes of at-risk children compared to normally developing children and children with SLI. A second aim was to assess differences within the groups. Finally, these results will be compared to those of the speech perception task. 3.1. Method 3.1.1. Participants Participants were 29 children at-risk of dyslexia (mean age 3;10, s.d. 3.6 months), 10 children with SLI (mean age 4;1, s.d. 3.2 months) and 10 control children (mean age 3;9, s.d. 3.3 months). These subjects came from the same pool of children as the perception study and met the same selection criteria. At age 5 non-verbal IQ was assessed with a Dutch standardised test (SON-R, Snijders et al., 1988). The mean IQ’s for the three groups were respectively 114 (at-risk), 104 (SLI), and 115 (controls). Differences on IQ between groups were not significant (F(2, 45) = 1.8, p = .19). 3.1.2. Test and stimulus materials Word production was assessed through a picture naming task. The naming task consisted of at least 50 productions of objects, including mono-, bi-, tri- and quadro-syllabic words, which were controlled for word stress, consonant clusters and consonant occurrence. The stimuli were checked for age of acquisition measured through AoA research (Ghyselinck, De Moor, & Brysbaert, 2000) as well as ratings by kindergarten teachers (Kohnstamm, Schaerlakens, de Vries, Akkerhuis, & Froonincksx, 1981). The age of acquisition of these words was generally less than 5 years. 3.1.3. Procedure The word production task was conducted in the same session as the categorisation task. It was presented through a game, in which the children were presented with a picture card. They had to name the depiction (for example, a cap) and features of the picture (a blue cap, with stripes), and then match the picture to the identical one on a game board. The game was completed upon having matched all picture cards. The children’s realisations, both of the naming task and any speech produced during this session, were recorded on DAT (Tascam DA-P1), with a sensitive microphone (Crown PZM-185). Data was transcribed phonetically by the second author. In order to check for accuracy, 10% of the data was transcribed by a second transcriber. Agreement was 91%.

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

187

3.1.4. Analysis The quantitative analyses included the percentage of consonants correct (PCC, Shriberg & Kwiatkowski, 1982) and the phonological mean length of utterance (PMLU, Ingram, 2002). The PPC is calculated by dividing the number of consonants produced correctly by the number of target phonemes, and multiplied by 100. The PCC was based on the accuracy of the identifiable words of the children, excluding pronouns, auxiliary verbs, proper nouns, yes/no responses, and onomatopoeia (e.g. tick-tock for clock, woof for dog). The mean PCC per child was calculated. The method of scoring for the PMLU is as follows: a vowel produced correctly is awarded one point. A correct consonant is awarded two points. The total score of the realisation (the child’s PMLU) is divided by the number of children’s realisations that are assessed. Unlike the PCC, this measure focuses on the child’s whole-word productions instead of specific segments. The PMLU takes into account that children generally first learn words, not individual sounds. The reason for including both PCC and PMLU is that the former has traditionally been assessed in at-risk studies and could be used for comparison. The PMLU is included as it takes the word level (complexity of the entire target) into account, which the PCC does not. It can thus be established whether PCC and PMLU lead to similar results. For the qualitative analyses, token counts3 were conducted on weak syllable truncation, consonant cluster avoidance (through reduction, omission, or epenthesis), dorsal avoidance (through substitution or omission), and fricative avoidance (idem). The counts on consonant cluster, dorsal- and fricative-avoidance were based on the occurrence in onset position of stressed or initial syllables, as prosodic positions have been known to influence the segmental productions of children (e.g. Chiat, 1983, 1989; Gnanadesikan, 2004). By selecting the onset or stressed syllable occurrences, the role of salience was kept constant. Avoidance counts include both substitution as well as omission. Consonant cluster avoidance was further divided into obstruent + sonorant cluster avoidance (e.g. blue ! bue) and/ s/ + consonant avoidance (e.g. snake ! nake). This division was made as it has been shown that the two types of clusters are acquired at different stages of acquisition and, when not yet acquired, might lead to different reductions (e.g. Barlow, 1997; Fikkert, 1994). Dorsal avoidance was measured by counting the number of instances of /k/ and /x/ in onset position of stressed and initial syllables and counting the times that the feature [dorsal] was changed. Substitution of /x/ to /k/, or vice versa, was not counted as incorrect, since the dorsal feature was maintained; however, substitution of /x/ to the [-dorsal] /t/ was counted as avoidance. Fricative avoidance involved the avoidance of the feature combination [-sonorant], [+continuant] in /s, z, f, v, x, S, Z/. Substitution of /f/ to /s/ was not counted as avoidance, as the continuant feature was maintained. Substitution of /f/ to for, example, the [-continuant] /t/ was counted as avoidance. Voicing differences were not taken into account, because many speakers of Dutch do not have a voicing distinction for fricatives in onset position. The fricatives were also analysed separately, as it has been found that they are acquired phoneme by phoneme rather than simultaneously as a class (e.g. Bernhardt & Stemberger, 1998). For the /f/, for example, substitution of /f/ to /s/ was counted as avoidance. It is thus possible that a child shows a low rate of fricative avoidance, but a high avoidance rate of individual fricatives. The /h/ was counted separately, but will not be taken into account here due to its ambiguous characteristics incorporating both + and  sonorant characteristics in Dutch and in child language acquisition (Booij, 1995; Fikkert, 1994). Separate counts of /S, Z/ were not taken into account either, due to their low occurrence in Dutch. 3.2. Results Table 2 presents the results of the PCC, the PMLU, as well as the qualitative counts. The data show that on all counts, performance is ranked: Control > Risk > SLI. The SLI group consistently reaches the highest avoidance scores. This was to be expected, as their speech and language difficulties have been severe enough to be diagnosed. The scores of the at-risk children are always in between the control and SLI group. With respect to the quantitative analyses, ANOVAs on arcsine-transformed PCC and PMLU with Group as factor are both significant (PCC (F(2, 48) = 6.2, p = .004) and PMLU (F(2, 48) = 7.9, p = .001). 3

Token counts calculate the occurrence of a process for each realisation, even if one word is realised more than once. Type counts, calculation of a process over all occurrences of one word, were also made. The differences between the types and token counts proved negligible.

188

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

Table 2 Performance of at-risk, language-impaired, and control groups on the speech production measures.

**

PCC PMLU** Weak syllable truncation **

Consonant cluster avoidance : Obstruent + sonorant avoidance** /s/ + consonant avoidance** Dorsal avoidance Fricative avoidance /s, z/ avoidance /x/ avoidance /f, v/ avoidance*

*

Control % (s.d.)

Risk % (s.d.)

SLI % (s.d.)

90.0 (6)a 8.9 (0.6)a

79.0 (16)ab 7.8 (1.4)a

66.0 (17)b 6.5 (1.6)

5.5 (5.84)

16.7 (19.0)

22.0 (24.5)

9.3 (5.2) 12.5 (6.4) 3.5 (7.6)

34.6 (30.2)a 38.0 (31.0)a 32.1 (34.1)a

60.0 (32.0)a 61.0 (32.6)a 57.1 (35.8)a

3.9 (5.4)

17.1 (25.1)

25.9 (35.3)

4.7 3.3 2.0 6.7

7.1 18.9 19.4 10.0

26.7 28.2 26.6 31.3

(6.0)a (10.5) (6.4) (10.0)a

(12.1)a (25.2) (34.8) (18.2)a

(39.3)a (42.4) (40.3) (37.6)a

Results are the mean % avoidance and the standard deviation. Values sharing the same ‘ab’ subscript do not differ significantly. * p < .05. ** p < .01.

Weak syllable truncation was not significant on a one-way analysis of variance (ANOVA) with group as betweensubjects factor and arcsine-transformed weak syllable truncation as within-subjects condition (F(2, 48) = 2.0, p = .15), despite the clearly visible trend of the staircase pattern (control > risk > SLI). The same holds true for dorsal avoidance (F(2, 48) = 2.0, p = .15). The at-risk and SLI group exhibit wide variation among the children on both weak-syllable truncation and dorsal avoidance, which might explain the lack of an effect. All three groups display most avoidance on consonant clusters. An ANOVA with group as between-subjects factor and arcsine-transformed consonant cluster avoidance as within-subjects condition reveals that there is a significant effect of consonant cluster avoidance (F(2, 48) = 7.1 p = .002). Games–Howell post hoc analyses establish that pairwise differences for control and SLI and control-risk were significant at the .05 level for consonant cluster avoidance. Within the at-risk group, consonant cluster avoidance ranged from 0% to 100%, pointing towards considerable variation within this group. For the control group, the range was more limited (2–18%) and only slightly more limited for the SLI group (15–100%). Consonant cluster simplification generally occurred through cluster reduction; epenthesis and omission were rare. The general finding on consonant cluster avoidance is mirrored in the results of the different cluster types; One-way ANOVAs for the two types of clusters found main effects for obstruent + sonorant (obs + son) avoidance (F(2, 48) = 6.0, p = .005) and /s/+C avoidance (F(2, 48) = 6.5, p = .003). Games–Howell post hocs show that differences of both obs + son and /s/+C avoidance were significant for control and SLI ( p < .01) and control and atrisk ( p < .01), but not for at-risk and SLI ( p > .1). Both types of clusters pose more difficulty for the at-risk and SLI group than for the control group. With respect to the fricatives, a significant effect can be found for arcsine-transformed fricative avoidance (F(2, 48) = 4.5, p = .02) and /f/ avoidance (F(2, 48) = 4.5, p = .02). Games–Howell post hocs show that there are no pairwise differences for fricative and /f/ avoidance. Despite the absence of significant effects, a trend is visible in that the controls outperform the at-risk who outperform the SLI group. Again, the considerable standard deviation might be preventing a significant effect. For both the fricative and dorsal counts, substitution was generally applied when the realisation was not target-like; omission was highly infrequent in all three groups. Unusual processes, such as backing of the front fricatives /f/ and /s/, was not a strategy employed by the children. 3.2.1. Individual data analysis In line with the arguments presented in the perception study, individual data analysis was conducted for the production study. As indicated above, the production data showed substantial variation among the at-risk group (and the SLI group), with a wide range in avoidance scores for each count (see Table 2). On the basis of these results, a

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

189

division was made between children performing well and those performing poorly on expressive phonology. This analysis of individual performance was based on the PCC scores (results on PMLU and composite scores of the phonological simplification counts rendered similar results; the analysis of the PCC was the most conservative). Calculations were made to see how many of the children performed at or above the control mean, up to 1s.d. below, and more than 1s.d. below the mean on PCC. As the data in Table 1 show, more than half of the at-risk group, and almost all SLI children belonged to the poorly performing group. Unlike the group of SLI children, a substantial proportion of the at-risk children performed similarly to the control mean. 3.3. Discussion The results of the expressive phonology task show that the at-risk group performs in-between the control and SLI group for both the quantitative and qualitative counts. Even though the results are not statistically significant for all simplification processes, a general trend is visible. These results suggest a mild delay in the speech production of the at-risk children as a group, blending in with previous findings (e.g. Carroll & Snowling, 2004; Locke et al., 1997; Scarborough, 1990). The fact that performance on weak syllable truncation does not lead to significant differences between the three groups, whereas most of the cluster and phoneme counts do, might imply that the global word representation of the words is relatively intact in the children, but that the more specified representations, such as syllable structure and phonemes, is more problematic. However, as there is substantial variation in degree of truncation in the at-risk (and SLI) group, it seems that intact word representations are not easily accessible for all children at this stage. Further investigation is thus necessary in relation to acquisition and exploitation of suprasegmental cues in these children. For all groups, correct production of consonant cluster structures proved most difficult of the processes measured. This is not surprising, as consonant cluster development is known for its protracted acquisition, in both typical and disordered development. However, compared to the control group, the at-risk and SLI groups had more difficulty acquiring the complexities of the syllable onset. The fact that more than half of the at-risk group was classified as ‘poor performers’ agrees with the statistic that 40– 60% of children with a familial risk of dyslexia become dyslexics themselves, which would be the children exhibiting speech and language difficulties. The finding that there are at-risk children who did not display difficulties in the areas of expressive phonology assessed here seems to conflict with the results of Elbro et al. (1998), who found that children with a risk of dyslexia always perform more poorly than controls on different speech tasks, even those children who did not develop reading difficulties. In contrast, a difference in speech performance of at-risk children who went on to develop dyslexia and those who did not, was found by Scarborough (1990). The unaffected at-risk children did not show poorer speech skills than the control children. The results of the current study seem to match those of Scarborough. However, this interpretation is tentative, as the children in the present study, contrary to those listed above, have not reached reading age yet. The results on expressive phonology can thus not yet be couched in a division based on reading skills, obstructing a direct comparison with the other studies. The current study has looked at processes typical of phonological development. Findings show that the three groups of children generally did not resort to unusual simplification strategies. The results suggest a delay in phonological production, as the processes the children apply are typical of normal child development. This finding can be couched in the SLI literature, which has reported abnormal or unlikely processes but generally finds that the majority of processes children with SLI use are typical, not deviant, simplification processes. The impact typical phonological simplification processes might have on subsequent reading skills cannot be ruled out. The general absence of unusual errors in the SLI group is at odds with the results of Leita˜o, Hogben, and Fletcher (1997) and Leita˜o and Fletcher (2004) who found that speech-impaired children who exhibited ‘non-developmental error processes’ at age 5–6, performed more poorly on phonological awareness and reading tasks at age 7 and age 12– 13 than those who applied developmental error patterns. It should be flagged here, however, that even in Leita˜o et al.’s sample, both speech-impaired children with normal and deviant phonological processes displayed difficulties with phonological awareness and spelling tasks. It is thus conceivable that speech-impaired children’s delayed phonological development also impacts on construction of phonological abilities and reading. Poor word production abilities on the word- syllable- and phoneme-level attested in the SLI and at-risk group might have a detrimental effect on the development of their phonological representations and awareness, which are

190

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

prerequisites for reading skills. However, the question whether these difficulties impact on future reading abilities cannot be answered yet. In light of the findings on the speech perception task, where speech perception difficulties were found in half of the at-risk group, the question arises whether these children are also the ones that exhibit poor speech production. Children with overlapping difficulties in perception and production might be the real-at risk children. This is line with the multirisk model of Vellutino et al. (2004), which argues that poor phonological processing carries the risk of overt dyslexia. The next section will compare the perception and production results. 4. Speech perception and production The first two parts of this paper have unmistakably shown that difficulties in both input and output phonology are present in children with a familial risk of dyslexia. The at-risk children as a group performed in-between the control and SLI children in both domains. The individual analyses revealed that not all, but respectively 56% and 55% of the at-risk children showed poor performance on the perception and production task. The individual analysis of the SLI group showed that more SLI children had production (90%) than perception difficulties (50%). In the control group, most children performed well in both domains, as expected. The separate input and output findings for the at-risk group fit in with two expectations: (1) phonological processing difficulties are involved in dyslexia and (2) 40–60% of the at-risk group develops dyslexia, suggesting that the 56% and 55% include the real at-risk children. This begs the question whether there is an overlap between perception and production performance. However, due to the fact that the tasks were part of a larger 2-h test battery, not all children in this study completed both the perception and the production task. Therefore, we only compared the data of the children who did. They were 7 control children, 18 at-risk children and 6 SLI children. These participant numbers are thus lower than those reported in Sections 2 and 3. On the basis of our group data and the results of previous studies we hypothesised that there would be few children with only poor speech perception scores. Thus, we expected to find children with a low score on speech sound categorisation and expressive phonology or only a low score on expressive phonology. Results are presented in Table 3. The labels + and  correspond with ‘good and 0–1s.d.’ and ‘poor’ as described in the sections on individual data analyses. Our expectations are supported by the data: the least occurring combination is poor perception with good production. Furthermore, 50% of the at-risk children display poor perception and production, compared with 33% of the SLI and 14% of the control children. An increased rate of phonological processing difficulties thus stands out for the at-risk group compared with the other groups. Additionally, the at-risk data in the table are highly similar to the group analysis: 56% of the at-risk children display perception difficulties and 61% show speech production problems, compared to 56% and 55% of the entire group. Despite the small size of the SLI group, performance of these six children is also highly similar to the group data: the number of children with speech production problems (83%) exceeds the number of children with speech perception difficulties (50%). None of the SLI children in Table 3 performed well in both domains. Results for the control group, finally, also match those of the groups as a whole. Furthermore, the results in the table confirm the hypothesis that an isolated speech perception impairment is rare. Nevertheless, one child in every group showed poor perception, but adequate production. The control child perceived most stimuli as ‘pop’ and only stimulus 6 and 7 were labelled ‘kop’ a few times; the child with SLI did the opposite and labelled most stimuli as ‘kop’. The at-risk child labelled the endpoint stimuli correctly approximately 70% of the time. These three labelling patterns result in a shallow slope and thus weak categorical perception. The production data of these three children did not display any delay/difficulties; only consonant-cluster production still reached 10–15% Table 3 Comparison of the subgroups of children with good or poor performance on stop-consonant categorisation and speech production. Group (N)

+Perception +production

+Perception production

Perception +production

Perception production

Control (N = 7) Risk (N = 18) SLI (N = 6)

71% 33% 0%

0% 11% 50%

14% 6% 17%

14% 50% 33%

Total (N = 31)

35%

16%

10%

39%

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

191

avoidance; truncation, dorsal- and fricative avoidance were all below this percentage. Thus, children with isolated speech perception difficulties constitute a minority in the data. For the at-risk group, poor perception is almost always accompanied by poor production skills (9/10 children). This suggests that generally the opposite picture emerges for this group; combined perception and production difficulties by far outrank an isolated speech perception impairment in this group. To conclude, the results of this subgroup of children with completed perception and production tasks are in line with the results of the entire groups presented in Sections 2.3 and 3.3. This implies that performance of children who completed only one task was similar to performance of the children who completed both tasks. Our findings are in accordance with the findings of Carroll and Snowling (2004) who also found affected speech perception and production in the at-risk population and the speech-impaired group. Furthermore, the study by Scarborough (1990) found that the at-risk children who showed poor perception and/or production were the ones to develop reading difficulties. It might be that the same holds for the children in the current study. 5. General discussion The results of the study can be summarised as follows. First, overall differences in categorical perception of /p/ and / k/ were found between 3-year-old children at familial risk of dyslexia, children with SLI, and age-matched controls. These results are consistent with (although more prominent than) previous studies with older dyslexic and language impaired children and dyslexic adults that used similar or other stop-consonant continua (e.g. Adlard & Hazan, 1998; Werker & Tees, 1987). Second, overall differences were found in speech production. On PCC and PMLU, as well as measures of truncation, consonant cluster-, fricative- and dorsal-avoidance, the control group outperformed the at-risk and SLI group, suggesting at least mildly delayed phonological behaviour of the latter groups. This blends in with previous atrisk findings and SLI literature. Both the perception and production study offer support for the notion that a phonological deficit underlies dyslexia. Third, individual data analysis in both perception and production domains established that about half of the at-risk and SLI children performed at least one standard deviation below the average of the control group. The individual data of the at-risk group shows that poor perception was attested in 56% of the group and poor production in 55%. These findings are similar to the 40–60% familial risk predicted in at-risk groups (Grigorenko, 2001). The individual SLI data revealed that only 50% had problems with speech sound categorisation, which suggests that SLI cannot primarily be explained by an underlying speech perception deficit. The individual production data in the SLI group showed that almost all children had poor speech production skills. These findings stress the need for thorough individual data analysis in order to fully understand the relationship between speech perception, language impairment and dyslexia. More research is warranted with larger groups of children and an extensive analysis of subgroups. Fourthly, a comparison of the perception and production data revealed that 90% of the at-risk children who were labelled as poor performers in the perceptual domain also had speech production problems. It is argued that these children could turn out to be the ‘true’ dyslexic or ‘high’-risk children. The present data provide subgroups of children that could be labelled as facing high risk and children with a lower risk. It seems likely that the children with additional speech perception and production problems are at a higher risk of developing dyslexia than the children without an additional speech processing disorder. This would support the multiple risk model of Vellutino et al. (2004). They propose that overt dyslexia emerges as the level of risk (including genetic disposition, environmental factors, and cognitive skills tapped on different levels) crosses a certain threshold. An important cognitive factor that might contribute to this cross-over could be impaired phonological processing. The present speech perception and production study tap into some of the phonological processes that have been shown to be impaired in much older dyslexics. An impairment of both speech perception and speech production might signal a higher risk than an impairment in none or in only one of these two domains. We will have to wait until these young at-risk children’s reading and writing instruction commences to test this hypothesis. Unfortunately, performance of only six SLI children could be included in the analysis of the relationship between speech perception and production. There were slightly more children with good perception and poor production. For SLI, a similar multirisk model could apply. This is proposed in Bishop’s multiple risk model that includes cognitive as well as environmental and familial risk factors to account for developmental language disorders (Bishop, 2003).

192

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

Even though these findings cannot yet contribute with great strength to the debate about the relationship between dyslexia and SLI, as literacy scores are not available yet, it seems that the notion of phonology presented in the models on dyslexia and SLI deserves further study, as, for example, not all SLI children suffered from poor speech perception. In sum, the present findings suggest that speech perception and production skills of 3-year-old children from dyslexic families might contribute to the risk of developing dyslexia at school age. The at-risk and SLI groups contain subgroups of poor and good performers in the speech perception or production domains. The at-risk children with an apparent and early deficit in both domains might be at high risk of literacy problems. The results indicate that both dyslexia and SLI can be explained by a multi-risk model which includes cognitive processes as well as genetic factors. Acknowledgements This study was supported by the Netherlands Organisation for Scientific Research (NWO, nr. 360-70-030), and the Utrecht institute of Linguistics OTS at Utrecht University. The authors wish to extend their gratitude to the children who participated in this research and to our collaborators, Petra van Alphen, Jan de Jong, Carien Wilsenach, and Frank Wijnen. Appendix A. Continuing education questions 1. The results of this study show that the SLI group is homogenous with respect to speech processing. (True or False) 2. The findings suggest that speech perception difficulties are present in all SLI children. (True or False) 3. With respect to speech production, there are no substantial differences between the pattern of results on the PPC/PMLU and phonological process counts. (True or False) 4. Results for a phonological deficit in speech production were mainly found by unusual error patterns in the at-risk and SLI groups. (True or False) 5. The findings of the perception and production study together plead for a unidirectional model of language impairment, with speech perception being an underlying cause. (True or False)

References Adlard, A., & Hazan, V. (1998). Speech perception in children with specific reading difficulties (dyslexia). The Quarterly Journal of Experimental Psychology, 51, 153–177. Aguilar-Mediavilla, E. M., Sanz-Torrent, M., & Serra-Ravento´s, M. (2002). A comparative study of the phonology of pre-school children with specific language impairment (SLI), language delay (LD) and normal acquisition. Clinical Linguistics and Phonetics, 6, 573–596. Alphen, P., van Bree, E., de Gerrits, E., Jong, J., de Wilsenach, C., & Wijnen, F. (2004). Early language development in children with a genetic risk for dyslexia. Dyslexia, 10, 265–288. Barlow, J. A. (1997). A constraint-based approach account of syllable onsets: Evidence from developing systems. Unpublished doctoral dissertation, Indiana University, USA. Beers, M. (1995). The phonology of normally developing and language-impaired children. Doctoral dissertation, University of Amsterdam, The Netherlands. Dordrecht: ICG Printing. Bernhardt, B. H., & Stemberger, J. P. (1998). Handbook of phonological development: From the perspective of constraint-based non-linear phonology. San Diego: Academic Press. Bishop, D. V. M. (2003). Genetic and environmental risks for specific language impairment in children. International Journal of Pediatric Otorhinolaryngology, 67s1, s143–s157. Bishop, D. V. M., & Snowling, M. J. (2004). Developmental dyslexia and specific language impairment: Same or different? Psychological Bulletin, 130, 858–886. Boets, B., Ghesquie`re, P., van Wieringen, A., & Wouters, J. (2006). Speech perception in preschoolers at family risk for dyslexia: Relations with lowlevel auditory processing and phonological ability. Brain and Language, 101, 19–30. Booij, G. (1995). The phonology of Dutch. Oxford: Clarendon Press. Carroll, J. M., & Snowling, M. J. (2004). Language and phonological skills in children at high risk of reading difficulties. Journal of Child Psychology and Psychiatry, 45, 631–640. Catts, H. W. (1986). Speech production/phonological deficits in reading disordered children. Journal of Learning Disabilities, 19, 504–508. Chiat, S. (1983). Why Mikey’s right and my key’s wrong: The significance of stress and word boundaries in a child’s output system. Cognition, 14, 275–300.

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

193

Chiat, S. (1989). The relation between prosodic structure, syllabification and segmental realization: Evidence from a child with fricative stopping. Clinical Linguistics and Phonetics, 3, 223–242. Coady, J. A., Kluender, K. R., & Evans, J. L. (2005). Categorical perception of speech by children with specific language impairment. Journal of Speech, Language and Hearing Research, 48, 944–959. Elbro, C., Borstrøm, I., & Petersen, D. K. (1998). Predicting dyslexia from kindergarten: The importance of distinctness of phonological representations of lexical items. Reading Research Quarterly, 3, 36–60. Fikkert, P. (1994). On the acquisition of prosodic structure. Doctoral Dissertation. University of Leiden, The Netherlands, HIL: IGC Printing Dordrecht. Gallagher, A., Frith, U., & Snowling, M. J. (2000). Precursors of literacy delay among children at familial risk of dyslexia. Journal of Child Psychology and Psychiatry, 41, 203–213. Gerrits, E. (2003). Speech perception of children at risk for dyslexia and children with specific language impairment. In M. J. Sole´, D. Recasens, & J. Romero (Eds.), In Proceedings of the 15th International Conference on Phonetic Sciences (pp. 2357–2360). Ghyselinck, M., De Moor, W., & Brysbaert, M. (2000). Age-of-acquisition ratings for 2816 Dutch four-and five-letter nouns. Psychologica Belga, 40, 77–98. Gnanadesikan, A. (2004). Markedness and faithfulness constraints in child phonology. In R. Kager, J. Pater, & W. Zonneveld (Eds.), Fixing priorities: Constraints in phonological acquisition (pp. 73–108). Cambridge: Cambridge University Press. Grigorenko, E. (2001). Developmental dyslexia: An update on genes, brains, and environments. Journal of Child Psychiatry and Psychology, 42, 91– 125. Ingram, D. (2002). The measurement of whole-word production. Journal of Child Language, 29, 713–733. Joanisse, M. F., Manis, F. R., Keating, P., & Seidenberg, M. S. (2000). Language deficits in dyslexic children: Speech perception, phonology, and morphology. Journal of Experimental Child Psychology, 77, 30–60. Joanisse, M. F., & Seidenberg, M. S. (1998). Specific language impairment: A deficit in grammar or processing? Trends in Cognitive Sciences, 2, 240–247. Kohnstamm, G. A., Schaerlakens, A. M., de Vries, A. K., Akkerhuis, G. W., & Froonincksx, M. (1981). Nieuwe streeflijst woordenschat. Lisse: Swets and Zeitlinger. Lambrecht Smith, S. (in press). Early phonological and lexical markers of reading disability. Reading and Writing: An Interdisciplinary Journal. Leita˜o, S., Hogben, J. H., & Fletcher, J. (1997). Phonological processing skills in speech and language impaired children. European Journal of Disorders of Communication, 32, 91–111. Leita˜o, S., & Fletcher, J. (2004). Literacy outcomes for students with speech impairment: Long-term follow-up. International Journal of Language and Communication Disorders, 39, 245–256. Locke, J. L., Hodgson, J., Macaruso, P., Roberts, S., Lambrecht-Smith, S., Guttentag, C. (1997). The development of developmental dyslexia. In C. Hulme, M. Snowling (Eds.), Dyslexia: Biology, cognition, and intervention. London: Whurr. Maassen, B., Groenen, P., Crul, T., Assman-Hulsmans, C., & Gabree¨ls, F. (2001). Identification and discrimination of voicing and place-ofarticulation in developmental dyslexia. Clinical Linguistics and Phonetics, 15, 319–339. Manis, F. R., McBride-Chang, C., Seidenberg, M. S., Keating, D., Doi, L. M., Munson, G., et al. (1997). Are speech perception deficits associated with developmental dyslexia? Journal of Experimental Child Psychology, 66, 211–235. McArthur, G. M., & Bishop, D. V. M. (2001). Auditory perceptual processing in people with reading and oral language impairments: Current issues and recommendations. Dyslexia, 7, 150–170. Miles, T., & Miles, E. (2001). Dyslexia, a hundred years on. Buckingham: Open University Press. Nathan, L., Stackhouse, J., Goulandris, N., & Snowling, M. J. (2004). The development of early literacy skills among children with speech difficulties: A test of the ‘Critical Age Hypothesis’. Journal of Speech, Language and Hearing Research, 47, 377–391. Orsolini, M., Sechi, E., Maronatoc, C., Bonvino, E., & Corcelli, A. (2001). The nature of phonological delay in children with specific language impairment. International Journal of Language and Communication Disorders, 36, 63–90. Ramus, F., Rosen, S., Dakin, S. C., Day, B. L., Castellote, J. M., White, S., et al. (2003). Theories of developmental dyslexia: Insights from a multiple case study of dyslexic adults. Brain, 126, 841–865. Richardson, U., Leppa¨nen, P. H. T., Leiwo, W., & Lyytinen, H. (2003). Perception of duration in infants with high genetic risk of dyslexia. Developmental Neuropsychology, 23, 387–397. Roberts, J., Rescorla, L., Giroux, J., & Stevens, L. (1998). Phonological skills of children with specific expressive language impairment (SLI-E): Outcome at age 3. Journal of Speech, Language, and Hearing Research, 41, 374–384. Scarborough, H. S. (1990). Very early language deficits in dyslexic children. Child Development, 61, 1728–1743. Serniclaes, W., Van Heghe, S., Mousty, Carre´, R., & Sprenger-Charolles, L. (2004). Allophonic mode of speech perception in dyslexia. Journal of Experimental Child Psychology, 87, 336–361. Shriberg, L. D., & Kwiatkowski, J. (1982). Phonological disorders III: A procedure for assessing severity of involvement. Journal of Speech and Hearing Research, 47, 256–270. Snowling, M. J. (1981). Phonemic deficits in developmental dyslexia. Psychological Research, 43, 219–234. Snowling, M., Bishop, D. V. M., & Stothard, S. E. (2000). Is preschool language impairment a risk factor for dyslexia in adolescence? Journal of Child Psychology and Psychiatry, 41, 587–600. Snijders, J. T., Tellegen, P. J., & Laros, J. A. (1988). Snijders-Oomen niet-verbale intelligentie test. Verantwoording en handleiding. Groningen: Wolters-Noordhoff. Stoel-Gammon, C., & Dunn, C. (1985). Normal and disordered phonology in children. Austin, Texas: Pro-Ed. Studdert-Kennedy, M. (2002). Deficits in phoneme awareness do not arise from failures in rapid auditory processing. Reading and Writing, 15, 5–14.

194

E. Gerrits, E. de Bree / Journal of Communication Disorders 42 (2009) 180–194

Sussman, J. E. (1993). Perception of formant transition cues to place of articulation in children with language impairments. Journal of Speech and Hearing Research, 36, 1286–1299. Swan, D., & Goswami, U. (1997). Picture naming deficits in developmental dyslexia: The phonological representations hypothesis. Brain and Language, 56, 334–353. Vellutino, F. R., Fletcher, J. M., Snowling, M. J., & Scanlon, D. M. (2004). Specific reading disability (Dyslexia): What have we learned in the past four decades? Journal of Child Psychology and Psychiatry, 45, 2–40. Werker, J. F., & Tees, R. C. (1987). Speech perception in severely disabled and average reading children. Canadian Journal of Psychology, 41, 48–61.