VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording

• URL: http://arxiv.org/abs/2107.07509v1
• Date: Thu, 15 Jul 2021 17:59:10 GMT
• Title: VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording
• Authors: Hirofumi Inaguma, Tatsuya Kawahara
• Abstract summary: We propose a block-synchronous beam search decoding to take advantage of efficient batched output-synchronous and low-latency input-synchronous searches. We also propose a VAD-free inference algorithm that leverages probabilities to determine a suitable timing to reset the model states. Experimental evaluations demonstrate that the block-synchronous decoding achieves comparable accuracy to the label-synchronous one.
• Score: 46.69852287267763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose novel decoding algorithms to enable streaming automatic speech recognition (ASR) on unsegmented long-form recordings without voice activity detection (VAD), based on monotonic chunkwise attention (MoChA) with an auxiliary connectionist temporal classification (CTC) objective. We propose a block-synchronous beam search decoding to take advantage of efficient batched output-synchronous and low-latency input-synchronous searches. We also propose a VAD-free inference algorithm that leverages CTC probabilities to determine a suitable timing to reset the model states to tackle the vulnerability to long-form data. Experimental evaluations demonstrate that the block-synchronous decoding achieves comparable accuracy to the label-synchronous one. Moreover, the VAD-free inference can recognize long-form speech robustly for up to a few hours.

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