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Evaluating automatic speech recognition systems as quantitative models of cross-lingual phonetic category perception
Thomas Schatz1, Francis Bach2, Emmanuel Dupoux3
1Department of Linguistics and UMIACS, University of Maryland, College Park, Maryland 20742, USA thomas.schatz@laposte.net.
The Journal of the Acoustical Society of America
|June 3, 2018
Summary
Listeners map foreign sounds to native phonetic categories using Automatic Speech Recognition (ASR) systems. This study proposes a novel computational method to quantify this mapping, aiding cross-linguistic phonetic research.
Area of Science:
- Linguistics
- Speech Science
- Computational Auditory Processing
Background:
- Cross-linguistic phonetic category perception theories suggest foreign sounds are mapped to native categories.
- A computational method for implementing this phonetic mapping has been lacking.
Purpose of the Study:
- To propose and validate a computational method for mapping foreign sounds to native phonetic categories.
- To utilize Automatic Speech Recognition (ASR) systems for phonetic mapping.
Main Methods:
- Employing ASR systems trained on continuous speech corpora to create a detailed mapping between foreign and native phonetic categories.
- Utilizing the machine ABX evaluation method to compare model predictions with human perception data.
Main Results:
- Successfully implemented a quantitative model for cross-linguistic phonetic mapping using ASR.
- Demonstrated the efficacy of the machine ABX method in evaluating phonetic perception models.
Conclusions:
- ASR systems offer a viable approach to implementing phonetic category mapping for foreign sounds.
- The proposed computational framework can quantitatively model and predict human cross-linguistic phonetic perception effects.
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