Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition

• URL: http://arxiv.org/abs/2107.01269v1
• Date: Fri, 2 Jul 2021 20:56:13 GMT
• Title: Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition
• Authors: Niko Moritz, Takaaki Hori, Jonathan Le Roux
• Abstract summary: Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks. However, the application of self-attention and attention-based encoder-decoder models remains challenging for streaming ASR. We present the dual causal/non-causal self-attention architecture, which in contrast to restricted self-attention prevents the overall context to grow beyond the look-ahead of a single layer.
• Score: 58.69803243323346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks. However, the application of self-attention and attention-based encoder-decoder models remains challenging for streaming ASR, where each word must be recognized shortly after it was spoken. In this work, we present the dual causal/non-causal self-attention (DCN) architecture, which in contrast to restricted self-attention prevents the overall context to grow beyond the look-ahead of a single layer when used in a deep architecture. DCN is compared to chunk-based and restricted self-attention using streaming transformer and conformer architectures, showing improved ASR performance over restricted self-attention and competitive ASR results compared to chunk-based self-attention, while providing the advantage of frame-synchronous processing. Combined with triggered attention, the proposed streaming end-to-end ASR systems obtained state-of-the-art results on the LibriSpeech, HKUST, and Switchboard ASR tasks.

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