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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

A perceptual similarity space for speech based on self-supervised speech representations.

Bronya R Chernyak¹, Ann R Bradlow², Joseph Keshet¹

¹Faculty of Electrical & Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

The Journal of the Acoustical Society of America

|June 21, 2024

Summary

This study introduces a new method for analyzing speech, using perceptual similarity spaces instead of traditional acoustic features. This approach better explains variations in speech intelligibility, especially for second-language (L2) speakers.

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Area of Science:

Speech processing
Acoustic phonetics
Machine learning

Background:

Speech recognition systems struggle with non-optimal conditions like background noise and second-language (L2) speech.
Existing methods analyzing spectro-temporal properties of speech leave much intelligibility variation unexplained.
A need exists for novel approaches to understand speech variability and improve recognition accuracy.

Purpose of the Study:

To investigate an alternative approach to speech analysis using perceptual similarity spaces.
To determine if perceptual similarity can explain intelligibility variations in L1 and L2 speech.
To assess the potential of self-supervised learning for encoding speech distinctions.

Main Methods:

Utilized self-supervised learning to create a perceptual similarity space for speech samples.
Encoded speech distinctions without relying on pre-defined acoustic features or speech-to-text alignment.
Quantified distances between first-language (L1) and L2 English speech samples in this space.

Main Results:

L2 English speech samples showed greater variability (less tight clustering) in the perceptual space compared to L1 samples.
Distances within the perceptual similarity space correlated with human listener performance.
L1 English listeners exhibited lower recognition accuracy for L2 speakers whose speech was more distant in the space.

Conclusions:

Perceptual similarity, captured by self-supervised learning, offers a powerful new dimension for speech analysis.
This approach provides a more comprehensive explanation for intelligibility variations than traditional feature-based methods.
Perceptual similarity spaces may form the foundation for next-generation speech and language analysis tools.