Language-Universal Speech Attributes Modeling for Zero-Shot Multilingual Spoken Keyword Recognition

We propose a novel language-universal approach to end-to-end automatic spoken keyword recognition (SKR) leveraging upon (i) a self-supervised pre-trained model, and (ii) a set of universal speech attributes (manner and place of articulation). Specifically, Wav2Vec2.0 is used to generate robust speec...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Yen, Hao, Ku, Pin-Jui, Sabato Marco Siniscalchi, Chin-Hui, Lee
Format: Article
Language:English
Subjects:
Error reduction
Languages
Phonemes
Speech
Online Access:Get full text
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Summary:We propose a novel language-universal approach to end-to-end automatic spoken keyword recognition (SKR) leveraging upon (i) a self-supervised pre-trained model, and (ii) a set of universal speech attributes (manner and place of articulation). Specifically, Wav2Vec2.0 is used to generate robust speech representations, followed by a linear output layer to produce attribute sequences. A non-trainable pronunciation model then maps sequences of attributes into spoken keywords in a multilingual setting. Experiments on the Multilingual Spoken Words Corpus show comparable performances to character- and phoneme-based SKR in seen languages. The inclusion of domain adversarial training (DAT) improves the proposed framework, outperforming both character- and phoneme-based SKR approaches with 13.73% and 17.22% relative word error rate (WER) reduction in seen languages, and achieves 32.14% and 19.92% WER reduction for unseen languages in zero-shot settings.
ISSN:2331-8422