Abstract

Automated processing and analysis of child speech has been long acknowledged as a harder problem compared to understanding speech by adults. Specifically, conversations between a child and adult involve spontaneous speech which often compounds idiosyncrasies associated with child speech. In this work, we improve upon the task of speaker diarization (determining who spoke when) from audio of child-adult conversations in naturalistic settings. We select conversations from the autism diagnosis and intervention domains, wherein speaker diarization forms an important step towards computational behavioral analysis in support of clinical research and decision making. We train deep speaker embeddings using publicly available child speech and adult speech corpora, unlike predominant state-of-art models which typically utilize only adult speech for speaker embedding training. We demonstrate …

Date

2021

Authors

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

Conference

2021 IEEE Spoken Language Technology Workshop (SLT)

Pages

590-597

Publisher

IEEE