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

• URL: http://arxiv.org/abs/2407.03495v1
• Date: Wed, 3 Jul 2024 20:51:41 GMT
• Title: Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations
• Authors: Kunal Dhawan, Nithin Rao Koluguri, Ante Jukić, Ryan Langman, Jagadeesh Balam, Boris Ginsburg,
• Abstract summary: We present a comprehensive analysis on building ASR systems with discrete codes. We investigate different methods for training such as quantization schemes and time-domain vs spectral feature encodings. We introduce a pipeline that outperforms Encodec at similar bit-rate.
• Score: 16.577870835480585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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