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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech.

Vijay Ravi¹, Jinhan Wang¹, Jonathan Flint²

¹Department of Electrical and Computer Engineering, University of California Los Angeles, California, USA 90095.

CEUR Workshop Proceedings

|April 23, 2024

Summary

This study introduces an unsupervised method for speaker disentanglement in speech-based depression detection, enhancing patient privacy. The novel approach improves depression detection accuracy while effectively masking speaker identity without needing speaker labels.

Keywords:

DAIC-WOZ Depression detection Healthcare AI Privacy Speaker disentanglement

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Area of Science:

Computational linguistics
Machine learning
Speech processing

Background:

Current depression detection from speech often requires speaker labels, leading to privacy concerns.
Existing methods can be unstable and add complexity through adversarial domain prediction.

Purpose of the Study:

To develop an unsupervised speaker disentanglement method for privacy-preserving depression detection.
To improve the accuracy and reduce the complexity of speech-based depression detection systems.

Main Methods:

An unsupervised approach reducing cosine similarity between latent spaces of depression and speaker classification models.
Utilizing ComparE16 features and an LSTM-only model on the DAIC-WOZ dataset.
Score-level fusion with a Word2vec-based text approach.

Main Results:

Achieved an F1-Score of 0.776 and a speaker de-identification (DeID) score of 92.87%, outperforming adversarial methods.
Demonstrated superior performance compared to an adversarial counterpart (F1-Score 0.762, DeID 68.37%).
Score-level fusion enhanced performance to an F1-Score of 0.830.

Conclusions:

The proposed unsupervised method enhances privacy in speech-based depression detection without speaker labels.
This approach reduces model complexity and improves performance over baseline and adversarial methods.
Speaker disentanglement is complementary to text-based methods, offering significant performance gains when combined.