Links

Disfluency in children with ASD and SLI

Our new article on disfluency in children with autism spectrum disorders (ASD) or specific language impairment (SLI) is now out in PLOS ONE. (The team consisted of Heather MacFarlane—who also did most of the annotation and much of the writing—myself, and Rosemary Ingham, Alison Presmanes Hill, Katina Papadakis, Géza Kiss, and Jan van Santen.)

There is a long-standing clinical impression that children with ASD are in some ways more disfluent than typically developing children, something likely related to their general difficulties with the set of abilities known as pragmatic language. We found that the few prior attempts to quantify this impression were difficult to interpret, and in some cases, put forth contradictory findings. One limitation that we observed in the prior work (other than poor controls and small samples, which one more or less expects in this area) is the lack of a well-thought-out schema for talking about different kinds of disfluency. While specialists in disfluency have largely operated “under the hypothesis that different types of disfluency manifest from different types of processing breakdowns”, so it is valuable to have a taxonomy of the types of disfluency so as to know what to count. Thus one of our goals in the paper is to adapt—to simplify, really—the schema used by Elizabeth Shriberg (in her 1995 UC Berkeley dissertation) and show that semi-skilled transcribers can achieve high rates of interannotator agreement using our schema. (We also show that much of the annotation can be automated, if one so chooses, and provide code for that.) Of course, we are even more interested in what we can learn about pragmatic language in children with ASD from our efforts at quantifying disfluency.

In in sample of 110 children with ASD, SLI, or typical development, we find two robust results. First, we found that children with ASD produced a higher ratio of content mazes (repetitions, revisions, and false starts) to fillers (e.g., uh, um) compared to their typically developing peers. Secondly, we found that children with ASD produced lower ratios of cued mazes—that is, content mazes that contain a filler—than their typically developing peers. We also found a suggestive result in a follow-up exploratory analysis: the use of cued mazes is positively correlated with chronological age in typically developing children (but not in children with ASD or SLI), which at least hints at a maturational account.

If you have anything to add, please feel free to leave post-publication comments at the PLOS one website.

Classifying paraphasias with NLP

I’m excited about our new article in the American Journal of Speech-Language Pathology (with Gerasimos Fergadiotis and Steven Bedrick) on automatic classification of paraphasias using basic natural language processing techniques.

Paraphasias are speech errors associated with aphasia. Roughly speaking, these errors may be phonologically similar to the target (dog for the target LOG) or dissimilar. They also may be semantically similar to the target (dog for the target CAT), or both (rat for the target CAT). Finally, they may be neologisms (tat for the target CAT). Finally, some paraphasias may be real words but neither phonologically nor semantically similar. The relative frequencies of these types of errors differ between people with aphasia. These can be measured in a confrontation naming task and, with complex and time-consuming manual error classification, used to create individualized profiles for treatment.

In the paper, we take archival data from a confrontation naming task and attempt to automate the classification of paraphasias. To quantify phonological similarity, we automate a series of baroque rules. To quantify semantic similarity, we use a computational model of semantic similarity (namely cosine similarity with word2vec embeddings). And, to identify neologisms, we use frequency in the SUBTLEX-US corpus. The results suggest that test scoring can in fact be automated with performance close to that of human annotators. With advances in speech recognition, it may soon be possible to develop a fully-automated computer-adaptive confrontation naming task in the near future!