Accurate and robust models for clinical speech processing

Samples of everyday conversations are being collected and analyzed in a growing number of applications, ranging from studying behavior in social psychology to clinical assessment of voice pathology and even cognitive function. Aside from the spoken words, the acoustic properties of speech samples can provide important cues in these applications. The goal of this study is to develop robust and accurate algorithms for estimating speech features.