Jan P. H. van Santen

The Hypothesis of Apraxia of Speech in Children with Autism Spectrum Disorder

In a sample of 46 children aged 4–7xa0years with Autism Spectrum Disorder (ASD) and intelligible speech, there was no statistical support for the hypothesis of concomitant Childhood Apraxia of Speech (CAS). Perceptual and acoustic measures of participants’ speech, prosody, and voice were compared with data from 40 typically-developing children, 13 preschool children with Speech Delay, and 15 participants aged 5–49xa0years with CAS in neurogenetic disorders. Speech Delay and Speech Errors, respectively, were modestly and substantially more prevalent in participants with ASD than reported population estimates. Double dissociations in speech, prosody, and voice impairments in ASD were interpreted as consistent with a speech attunement framework, rather than with the motor speech impairments that define CAS.

Computational prosodic markers for autism

We present results obtained with new instrumental methods for the acoustic analysis of prosody to evaluate prosody production by children with Autism Spectrum Disorder (ASD) and Typical Development (TD). Two tasks elicit focal stress - one in a vocal imitation paradigm, the other in a picture-description paradigm; a third task also uses a vocal imitation paradigm, and requires repeating stress patterns of two-syllable nonsense words. The instrumental methods differentiated significantly between the ASD and TD groups in all but the focal stress imitation task. The methods also showed smaller differences in the two vocal imitation tasks than in the picture-description task, as was predicted. In fact, in the nonsense word stress repetition task, the instrumental methods showed better performance for the ASD group. The methods also revealed that the acoustic features that predict auditory-perceptual judgment are not the same as those that differentiate between groups. Specifically, a key difference between the groups appears to be a difference in the balance between the various prosodic cues, such as pitch, amplitude, and duration, and not necessarily a difference in the strength or clarity with which prosodic contrasts are expressed.

Automated assessment of prosody production

Assessment of prosody is important for diagnosis and remediation of speech and language disorders, for diagnosis of neurological conditions, and for foreign language instruction. Current assessment is largely auditory-perceptual, which has obvious drawbacks; however, automation of assessment faces numerous obstacles. We propose methods for automatically assessing production of lexical stress, focus, phrasing, pragmatic style, and vocal affect. Speech was analyzed from children in six tasks designed to elicit specific prosodic contrasts. The methods involve dynamic and global features, using spectral, fundamental frequency, and temporal information. The automatically computed scores were validated against mean scores from judges who, in all but one task, listened to prosodic minimal pairs of recordings, each pair containing two utterances from the same child with approximately the same phonemic material but differing on a specific prosodic dimension, such as stress. The judges identified the prosodic categories of the two utterances and rated the strength of their contrast. For almost all tasks, we found that the automated scores correlated with the mean scores approximately as well as the judges individual scores. Real-time scores assigned during examination - as is fairly typical in speech assessment - correlated substantially less than the automated scores with the mean scores.

Quantifying Repetitive Speech in Autism Spectrum Disorders and Language Impairment

We report on an automatic technique for quantifying two types of repetitive speech: repetitions of what the child says him/herself (self‐repeats) and of what is uttered by an interlocutor (echolalia). We apply this technique to a sample of 111 children between the ages of four and eight: 42 typically developing children (TD), 19 children with specific language impairment (SLI), 25 children with autism spectrum disorders (ASD) plus language impairment (ALI), and 25 children with ASD with normal, non‐impaired language (ALN). The results indicate robust differences in echolalia between the TD and ASD groups as a whole (ALNu2009+u2009ALI), and between TD and ALN children. There were no significant differences between ALI and SLI children for echolalia or self‐repetitions. The results confirm previous findings that children with ASD repeat the language of others more than other populations of children. On the other hand, self‐repetition does not appear to be significantly more frequent in ASD, nor does it matter whether the childs echolalia occurred within one (immediate) or two turns (near‐immediate) of the adults original utterance. Furthermore, non‐significant differences between ALN and SLI, between TD and SLI, and between ALI and TD are suggestive that echolalia may not be specific to ALN or to ASD in general. One important innovation of this work is an objective fully automatic technique for assessing the amount of repetition in a transcript of a childs utterances. Autism Res 2013, ●●: ●●–●●.

Automatic measurement of affective valence and arousal in speech

Methods are proposed for measuring affective valence and arousal in speech. The methods apply support vector regression to prosodic and text features to predict human valence and arousal ratings of three stimulus types: speech, delexicalized speech, and text transcripts. Text features are extracted from transcripts via a lookup table listing per-word valence and arousal values and computing per-utterance statistics from the per-word values. Prediction of arousal ratings of delexicalized speech and of speech from prosodic features was successful, with accuracy levels not far from limits set by the reliability of the human ratings. Prediction of valence for these stimulus types as well as prediction of both dimensions for text stimuli proved more difficult, even though the corresponding human ratings were as reliable. Text based features did add, however, to the accuracy of prediction of valence for speech stimuli. We conclude that arousal of speech can be measured reliably, but not valence, and that improving the latter requires better lexical features.

Detecting linguistic idiosyncratic interests in autism using distributional semantic models

Children with autism spectrum disorder often exhibit idiosyncratic patterns of behaviors and interests. In this paper, we focus on measuring the presence of idiosyncratic interests at the linguistic level in children with autism using distributional semantic models. We model the semantic space of children’s narratives by calculating pairwise word overlap, and we compare the overlap found within and across diagnostic groups. We find that the words used by children with typical development tend to be used by other children with typical development, while the words used by children with autism overlap less with those used by children with typical development and even less with those used by other children with autism. These findings suggest that children with autism are veering not only away from the topic of the target narrative but also in idiosyncratic semantic directions potentially defined by their individual topics of interest.

A comparison of different dimensionality reduction and feature selection methods for single trial ERP detection

Dimensionality reduction and feature selection is an important aspect of electroencephalography based event related potential detection systems such as brain computer interfaces. In our study, a predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A linear discriminant analysis (LDA) classifier was designed as the ERP (Event Related Potential) detector for its simplicity. Different dimensionality reduction and feature selection methods were applied and compared in a greedy wrapper framework. Experimental results showed that PCA with the first 10 principal components for each channel performed best and could be used in both online and offline systems.