New Computer Program Can Identify Communication Problems in Children

Most people I've talked with have probably notice at some point or other that I've got a speech impediment: a stutter. I won't blame anyone if they haven't noticed, however, because the darn thing likes to come and go as it pleases and manifests itself sporadically.

It has, at times, made some things (ordering at drive-throughs, for example) more difficult for me, but for the most part I'd say I've got it under control. This is due in part to a few years of speech therapy I went through in grade school. Taming it might've been easier to do had I started therapy earlier. Unfortunately, upwards of 60% of kids are like I was--they aren't diagnosed with their speech or language disorders before years after kindergarten, and as a result get less time to benefit from treatment.

That's why I'm intrigued by the work of researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital’s Institute of Health Professions.
Earlier this week at Interspeech, a conference on speech processing, the researchers reported on the results of recent experiments with a computer system they designed to not only identify language and speech disorders, but also recommend what courses of actions should be taken.

Pretty cool, I guess, but I wondered: Couldn't you tell if a kid has speaking problems by just, well, listening to him or her? Well, that's exactly what the computer system does. It analyzes audio recordings of children reading a story and looks for irregularities in their speech patterns.

To teach the system how to do this, researchers John Guttag and Jen Gong used a method called machine learning, in which a computer searches huge sets of data "for patterns that correspond to particular classifications."

That data was gathered by Tiffany Hogan and Jordan Green, scientists who helped design the program. They said it mainly looks for pauses in the child's speech when he or she is reading.

The MIT researchers even took age and gender into account when training the computer system using a technique called residual analysis. After identifying correlations between the gender and age of test subjects and the "acoustic features" of the way they spoke, Gong corrected for those correlations before inserting the data into the machine-learning program.

All things considered, this interesting development may prove vital in identifying and correcting people's language and speech problems in the future.

Sources

• Automated screening for childhood communication disorders--MIT News
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