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arXiv · 2302.09908

A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One

Lingwei Meng·Jiawen Kang·Mingyu Cui·Yuejiao Wang·Xixin Wu·Helen Meng
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Abstract

Although automatic speech recognition (ASR) can perform well in common non-overlapping environments, sustaining performance in multi-talker overlapping speech recognition remains challenging. Recent research revealed that ASR model's encoder captures different levels of information with different layers -- the lower layers tend to have more acoustic information, and the upper layers more linguistic. This inspires us to develop a Sidecar separator to empower a well-trained ASR model for multi-talker scenarios by separating the mixed speech embedding between two suitable layers. We experimented with a wav2vec 2.0-based ASR model with a Sidecar mounted. By freezing the parameters of the original model and training only the Sidecar (8.7 M, 8.4% of all parameters), the proposed approach outperforms the previous state-of-the-art by a large margin for the 2-speaker mixed LibriMix dataset, reaching a word error rate (WER) of 10.36%; and obtains comparable results (7.56%) for LibriSpeechMix dataset when limited training.

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Lingwei Meng, Jiawen Kang, Mingyu Cui, Yuejiao Wang, Xixin Wu, Helen Meng. 2023-02-20. A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One. https://arxiv.org/abs/2302.09908

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