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arXiv · 2309.04654

Mask-CTC-based Encoder Pre-training for Streaming End-to-End Speech Recognition

Huaibo Zhao·Yosuke Higuchi·Yusuke Kida·Tetsuji Ogawa·Tetsunori Kobayashi
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Abstract

Achieving high accuracy with low latency has always been a challenge in streaming end-to-end automatic speech recognition (ASR) systems. By attending to more future contexts, a streaming ASR model achieves higher accuracy but results in larger latency, which hurts the streaming performance. In the Mask-CTC framework, an encoder network is trained to learn the feature representation that anticipates long-term contexts, which is desirable for streaming ASR. Mask-CTC-based encoder pre-training has been shown beneficial in achieving low latency and high accuracy for triggered attention-based ASR. However, the effectiveness of this method has not been demonstrated for various model architectures, nor has it been verified that the encoder has the expected look-ahead capability to reduce latency. This study, therefore, examines the effectiveness of Mask-CTCbased pre-training for models with different architectures, such as Transformer-Transducer and contextual block streaming ASR. We also discuss the effect of the proposed pre-training method on obtaining accurate output spike timing.

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Huaibo Zhao, Yosuke Higuchi, Yusuke Kida, Tetsuji Ogawa, Tetsunori Kobayashi. 2023-09-09. Mask-CTC-based Encoder Pre-training for Streaming End-to-End Speech Recognition. https://arxiv.org/abs/2309.04654

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