Improving RNN Transducer Based ASR with Auxiliary Tasks

• URL: http://arxiv.org/abs/2011.03109v2
• Date: Mon, 9 Nov 2020 03:48:00 GMT
• Title: Improving RNN Transducer Based ASR with Auxiliary Tasks
• Authors: Chunxi Liu, Frank Zhang, Duc Le, Suyoun Kim, Yatharth Saraf, Geoffrey Zweig
• Abstract summary: End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results. In this work, we examine ways in which recurrent neural network transducer (RNN-T) can achieve better ASR accuracy via performing auxiliary tasks.
• Score: 21.60022481898402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers. Specifically, recurrent neural network transducer (RNN-T) has shown competitive ASR performance on various benchmarks. In this work, we examine ways in which RNN-T can achieve better ASR accuracy via performing auxiliary tasks. We propose (i) using the same auxiliary task as primary RNN-T ASR task, and (ii) performing context-dependent graphemic state prediction as in conventional hybrid modeling. In transcribing social media videos with varying training data size, we first evaluate the streaming ASR performance on three languages: Romanian, Turkish and German. We find that both proposed methods provide consistent improvements. Next, we observe that both auxiliary tasks demonstrate efficacy in learning deep transformer encoders for RNN-T criterion, thus achieving competitive results - 2.0%/4.2% WER on LibriSpeech test-clean/other - as compared to prior top performing models.

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