Efficient End-to-End Speech Recognition Using Performers in Conformers

• URL: http://arxiv.org/abs/2011.04196v2
• Date: Wed, 11 Nov 2020 02:07:46 GMT
• Title: Efficient End-to-End Speech Recognition Using Performers in Conformers
• Authors: Peidong Wang, DeLiang Wang
• Abstract summary: We propose to reduce the complexity of model architectures in addition to model sizes. The proposed model yields competitive performance on the LibriSpeech corpus with 10 millions of parameters and linear complexity.
• Score: 74.71219757585841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On-device end-to-end speech recognition poses a high requirement on model efficiency. Most prior works improve the efficiency by reducing model sizes. We propose to reduce the complexity of model architectures in addition to model sizes. More specifically, we reduce the floating-point operations in conformer by replacing the transformer module with a performer. The proposed attention-based efficient end-to-end speech recognition model yields competitive performance on the LibriSpeech corpus with 10 millions of parameters and linear computation complexity. The proposed model also outperforms previous lightweight end-to-end models by about 20% relatively in word error rate.

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