Transformer Based Deliberation for Two-Pass Speech Recognition

• URL: http://arxiv.org/abs/2101.11577v1
• Date: Wed, 27 Jan 2021 18:05:22 GMT
• Title: Transformer Based Deliberation for Two-Pass Speech Recognition
• Authors: Ke Hu, Ruoming Pang, Tara N. Sainath, Trevor Strohman
• Abstract summary: Speech recognition systems must generate words quickly while also producing accurate results. Two-pass models excel at these requirements by employing a first-pass decoder that quickly emits words, and a second-pass decoder that requires more context but is more accurate. Previous work has established that a deliberation network can be an effective second-pass model.
• Score: 46.86118010771703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interactive speech recognition systems must generate words quickly while also producing accurate results. Two-pass models excel at these requirements by employing a first-pass decoder that quickly emits words, and a second-pass decoder that requires more context but is more accurate. Previous work has established that a deliberation network can be an effective second-pass model. The model attends to two kinds of inputs at once: encoded audio frames and the hypothesis text from the first-pass model. In this work, we explore using transformer layers instead of long-short term memory (LSTM) layers for deliberation rescoring. In transformer layers, we generalize the "encoder-decoder" attention to attend to both encoded audio and first-pass text hypotheses. The output context vectors are then combined by a merger layer. Compared to LSTM-based deliberation, our best transformer deliberation achieves 7% relative word error rate improvements along with a 38% reduction in computation. We also compare against non-deliberation transformer rescoring, and find a 9% relative improvement.

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