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- W3006898571 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." @default.
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- W3006898571 date "2020-02-01" @default.
- W3006898571 modified "2023-10-14" @default.
- W3006898571 title "Improving speaker diarization for naturalistic child-adult conversational interactions using contextual information" @default.
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- W3006898571 doi "https://doi.org/10.1121/10.0000736" @default.
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