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Related Experiment Video

Updated: May 30, 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

Partially supervised speaker clustering.

Hao Tang¹, Stephen Mingyu Chu, Mark Hasegawa-Johnson

¹HP Labs, 1501 Page Mill Road, Palo Alto, CA 94304, USA. hao.tang@hp.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 17, 2011

Summary

This study introduces partially supervised speaker clustering, enhancing audio stream analysis. Leveraging prior speaker knowledge and a novel Linear Spherical Discriminant Analysis (LSDA) method improves accuracy significantly.

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

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

Published on: September 27, 2024

Related Experiment Videos

Last Updated: May 30, 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

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

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

Published on: September 27, 2024

Area of Science:

Speech processing and machine learning.
Multimedia information retrieval and database management.

Background:

Structuring multimedia content, including audio, is crucial for indexing and retrieval.
Speaker clustering, assigning speech utterances to speakers, is a key challenge in audio stream analysis.

Purpose of the Study:

To develop and evaluate a partially supervised speaker clustering approach.
To improve speaker clustering accuracy by incorporating prior speaker knowledge.

Main Methods:

Learning speaker-discriminative acoustic feature transformations, universal speaker prior models, and discriminative speaker subspaces.
Utilizing Gaussian Mixture Model (GMM) mean supervector representations and advocating for the cosine distance metric.
Proposing Linear Spherical Discriminant Analysis (LSDA) within a graph embedding framework for distance metric learning.

Main Results:

Speaker clustering methods based on GMM mean supervectors and vector-based metrics outperform traditional approaches.
The cosine distance metric consistently improves performance over the Euclidean distance metric.
Partially supervised strategies and the proposed LSDA algorithm significantly enhance speaker clustering accuracy, achieving state-of-the-art results.

Conclusions:

Partially supervised learning effectively leverages prior knowledge to boost speaker clustering performance.
The cosine distance metric and LSDA offer significant advantages for speaker clustering in high-dimensional spaces.
The proposed methods represent a substantial advancement in speaker clustering technology.