Non-autoregressive Deliberation-Attention based End-to-End ASR

Attention-based encoder-decoder end-to-end (E2E) automatic speech recognition (ASR) architectures have achieved the state-of-the-art results on many ASR tasks. However, the conventional attention-based E2E ASR models rely on the autoregressive decoder, which makes the parallel computation in decoding difficult. In this paper, we propose a novel deliberation-attention (D-Att) based E2E ASR architecture, which replaces the autoregressive attention-based decoder with the non-autoregressive frame level D-Att decoder, and thus accelerates the GPU parallel decoding speed significantly. D-Att decoder differs from the conventional attention decoder on two aspects: first, D-Att decoder uses the frame level text embedding (FLTE) generated by an auxiliary ASR model instead of the ground truth transcripts or previous predictions which are required by the conventional attention decoder; second, conventional attention decoder is trained in the left-to-right label-synchronous way, however, D-Att decoder is trained under the supervision of connectionist temporal classification (CTC) loss and utilizes the FLTE to provide the text information. Our experiments on Aishell, HKUST and WSJ benchmarks show that the proposed D-Att E2E ASR models are comparable to the performance of the state-of-the-art autoregressive attention-based transformer E2E ASR baselines, and are 10 times faster with GPU parallel decoding.