SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition

• URL: http://arxiv.org/abs/2110.05571v1
• Date: Mon, 11 Oct 2021 19:23:50 GMT
• Title: SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition
• Authors: Jing Pan, Tao Lei, Kwangyoun Kim, Kyu Han, Shinji Watanabe
• Abstract summary: We present the advantages of applying SRU++ in ASR tasks by comparing with Conformer across multiple ASR benchmarks. Specifically, SRU++ can surpass Conformer on long-form speech input with a large margin, based on our analysis.
• Score: 49.42625022146008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention mechanism excels in capturing long-range dependencies. While models built solely upon attention can be better parallelized than regular RNN, a novel network architecture, SRU++, was recently proposed. By combining the fast recurrence and attention mechanism, SRU++ exhibits strong capability in sequence modeling and achieves near-state-of-the-art results in various language modeling and machine translation tasks with improved compute efficiency. In this work, we present the advantages of applying SRU++ in ASR tasks by comparing with Conformer across multiple ASR benchmarks and study how the benefits can be generalized to long-form speech inputs. On the popular LibriSpeech benchmark, our SRU++ model achieves 2.0% / 4.7% WER on test-clean / test-other, showing competitive performances compared with the state-of-the-art Conformer encoder under the same set-up. Specifically, SRU++ can surpass Conformer on long-form speech input with a large margin, based on our analysis.

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