Abstract

While deep learning has driven recent improvements in audio speaker diarization, it often faces performance issues in challenging interaction scenarios and varied acoustic settings such as between a child and adult (caregiver/examiner). In this work, the role of contextual factors that affect diarization performance in such interactions is analyzed. Factors that affect each type of diarization error are identified. Furthermore, a DNN is trained on diarization outputs in conjunction with the factors to improve diarization performance. The results demonstrate the usefulness of incorporating context in improving diarization performance of child-adult interactions in clinical settings.

Date

2020

Authors

Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan

Journal

The Journal of the Acoustical Society of America

Volume

147

Issue

2

Pages

EL196-EL200

Publisher

AIP Publishing