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arXiv · 2407.03495

Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations

Kunal Dhawan·Nithin Rao Koluguri·Ante Jukić·Ryan Langman·Jagadeesh Balam·Boris Ginsburg
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Abstract

Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech recognition (ASR), translation, speaker verification, and joint speech-text foundational models. In this work, we present a comprehensive analysis on building ASR systems with discrete codes. We investigate different methods for codec training such as quantization schemes and time-domain vs spectral feature encodings. We further explore ASR training techniques aimed at enhancing performance, training efficiency, and noise robustness. Drawing upon our findings, we introduce a codec ASR pipeline that outperforms Encodec at similar bit-rate. Remarkably, it also surpasses the state-of-the-art results achieved by strong self-supervised models on the 143 languages ML-SUPERB benchmark despite being smaller in size and pretrained on significantly less data.

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Kunal Dhawan, Nithin Rao Koluguri, Ante Jukić, Ryan Langman, Jagadeesh Balam, Boris Ginsburg. 2024-07-03. Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations. https://doi.org/10.21437/interspeech.2024-330

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