A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model

• URL: http://arxiv.org/abs/2401.02673v1
• Date: Fri, 5 Jan 2024 07:11:13 GMT
• Title: A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model
• Authors: Dongdi Zhao, Jianbo Ma, Lu Lu, Jinke Li, Xuan Ji, Lei Zhu, Fuming Fang, Ming Liu, Feijun Jiang
• Abstract summary: We propose a unified multichannel far-field speech recognition system that combines the neural beamforming and transformer-based Listen, Spell, Attend (LAS) speech recognition system. The proposed method achieve 19.26% improvement when compared with a strong baseline.
• Score: 14.795953417531907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Far-field speech recognition is a challenging task that conventionally uses signal processing beamforming to attack noise and interference problem. But the performance has been found usually limited due to heavy reliance on environmental assumption. In this paper, we propose a unified multichannel far-field speech recognition system that combines the neural beamforming and transformer-based Listen, Spell, Attend (LAS) speech recognition system, which extends the end-to-end speech recognition system further to include speech enhancement. Such framework is then jointly trained to optimize the final objective of interest. Specifically, factored complex linear projection (fCLP) has been adopted to form the neural beamforming. Several pooling strategies to combine look directions are then compared in order to find the optimal approach. Moreover, information of the source direction is also integrated in the beamforming to explore the usefulness of source direction as a prior, which is usually available especially in multi-modality scenario. Experiments on different microphone array geometry are conducted to evaluate the robustness against spacing variance of microphone array. Large in-house databases are used to evaluate the effectiveness of the proposed framework and the proposed method achieve 19.26\% improvement when compared with a strong baseline.

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