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Semi-supervised audio-driven TV-news speaker diarization using deep neural embeddings.

Nikolaos Tsipas¹, Lazaros Vrysis¹, Konstantinos Konstantoudakis¹

¹Aristotle University of Thessaloniki, Thessaloniki, Greece.

The Journal of the Acoustical Society of America

|December 31, 2020

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This study introduces a novel audio-visual method for speaker diarization, enhancing multimedia content analysis. The approach uses deep learning embeddings and a fusion stage for improved accuracy in identifying different speakers.

Area of Science:

Computer Science
Artificial Intelligence
Multimedia Analysis

Background:

Speaker diarization is crucial for understanding multimedia content.
Existing methods often struggle with complex audio-visual data.
Accurate speaker identification enhances content accessibility and analysis.

Purpose of the Study:

To introduce and evaluate an audio-driven, multimodal approach for speaker diarization.
To leverage deep learning for generating audio-visual embeddings.
To improve speaker diarization performance through a novel fusion stage.

Main Methods:

Semi-supervised clustering of audio-visual embeddings.
Utilizing Long Short-Term Memory (LSTM) Siamese networks for audio embeddings.

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Employing pre-trained Convolutional Neural Networks (CNNs) for video-based facial embeddings.

Implementing a fusion stage combining audio and video information.

Main Results:

The proposed multimodal approach demonstrates improved speaker diarization performance.
Evaluation on large-scale datasets (VoxCeleb, AVL-SD) validates the method's effectiveness.
The system successfully handles peculiarities of TV news content.

Conclusions:

The developed audio-visual speaker diarization method offers a significant advancement.
The open-source release promotes reproducible research and collaboration.
This approach enhances the analysis of multimedia content by accurately identifying speakers.