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Related Experiment Video

Updated: May 11, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Towards online maximum-likelihood-based speech clustering and separation.

Mehrez Souden¹, Keisuke Kinoshita, Tomohiro Nakatani

¹NTT Communication Science Laboratories, 2-4 Hikaridai Seika-cho, 619-0237 Kyoto, Japan. msouden6@ece.gatech.edu

The Journal of the Acoustical Society of America

|May 10, 2013

Summary

This summary is machine-generated.

This study presents an online approach for speech clustering and separation using multichannel location information. The recursive expectation maximization algorithm continuously adjusts speech clusters, outperforming batch methods with moving speakers.

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Last Updated: May 11, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

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Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Published on: September 27, 2024

Area of Science:

Signal Processing
Acoustics
Machine Learning

Background:

Speech source clustering and separation are crucial for audio processing.
Existing batch methods struggle with dynamic acoustic environments and speaker movement.
Multichannel location information offers potential for improved source localization and separation.

Purpose of the Study:

To introduce an online approach for speech source clustering and separation.
To leverage multichannel location information within a recursive expectation maximization (EM) algorithm.
To enable continuous adaptation of speech clusters in real-time.

Main Methods:

Utilized normalized multichannel speech-recording vectors as feature vectors.
Modeled feature vectors using the Watson mixture model.
Employed a recursive expectation maximization (EM) algorithm for online parameter estimation by maximizing data likelihood at each time-frequency slot.

Main Results:

The proposed online approach demonstrated continuous adjustment of speech clusters.
Promising results were obtained, showing advantages over the batch EM algorithm.
Effectiveness was validated in scenarios with two speakers exhibiting movement.

Conclusions:

The developed online EM algorithm effectively performs speech source clustering and separation.
The approach offers superior performance compared to batch methods, especially in dynamic speaker scenarios.
This method provides a robust solution for real-time audio processing with moving sound sources.