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., uhum) 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.

Using the P2FA/FAVE-align SCOTUS acoustic models in Prosodylab-Aligner

Chris Landreth writes in with a tip on how to use the SCOTUS Corpus acoustic model (the one used in P2FA and FAVE-align) from within Prosodylab-Aligner. This is as simple as downloading the data and modifying the YAML configuration file and placing the model data in the right place. Here is the 16k model.

To use it, simply download into your working directory and then execute something like the following:

python3 -m aligner -r eng-SCOTUS-16k.zip -a yrdata -d eng.dict

Please let me know if you have any problems with that.