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Speaker-turn aware diarization for speech-based cognitive assessments.

Sean Shensheng Xu¹, Xiaoquan Ke², Man-Wai Mak²

¹School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.

Frontiers in Neuroscience

|January 31, 2024

Summary

This study enhances speaker diarization for cognitive assessments. The new method improves accuracy in challenging conditions and can identify speaker turns, aiding analysis of speech data.

Keywords:

MOCA comprehensive scoring dementia detection speaker diarization speaker embedding speaker-turn timestamps

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Area of Science:

Speech processing
Cognitive science
Biomedical engineering

Background:

Speaker diarization is crucial for analyzing speech in cognitive assessments like the Montreal cognitive assessment (MoCA).
Conventional methods face challenges with acoustic mismatch and accurately identifying speaker turns in MoCA recordings.

Purpose of the Study:

To propose and evaluate three enhancements for speaker diarization in MoCA recordings.
To improve diarization accuracy and robustness against various mismatch conditions.
To enable speaker-turn timestamp hypothesization for datasets lacking this information.

Main Methods:

Utilizing multi-scale channel interdependence speaker embedding with Res2Net blocks, squeeze-and-excitation units, and channel-dependent attention.
Applying a sequence comparison approach for a holistic conversational view to create a speaker-turn aware scoring matrix.
Incorporating a pairwise similarity measure to integrate local and global information into the scoring matrix.

Main Results:

The proposed enhancements significantly outperform conventional x-vector/PLDA systems in speaker diarization.
Demonstrated superior performance under language-, age-, and microphone-mismatch scenarios.
Successfully hypothesized speaker-turn timestamps, enhancing applicability to diverse datasets.

Conclusions:

The developed speaker diarization system offers improved accuracy and robustness for cognitive impairment diagnosis from speech.
The method's ability to hypothesize speaker turns makes it valuable for analyzing speech datasets, even those without explicit timestamp information.