Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset

• URL: http://arxiv.org/abs/2010.11395v3
• Date: Sun, 28 Feb 2021 08:02:29 GMT
• Title: Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset
• Authors: Xie Chen, Yu Wu, Zhenghao Wang, Shujie Liu, Jinyu Li
• Abstract summary: Transformer Transducer (T-T) models for the fist pass decoding with low latency and fast speed on a large-scale dataset. We combine the idea of Transformer-XL and chunk-wise streaming processing to design a streamable Transformer Transducer model. We demonstrate that T-T outperforms the hybrid model, RNN Transducer (RNN-T), and streamable Transformer attention-based encoder-decoder model in the streaming scenario.
• Score: 37.619200507404145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Transformer based end-to-end models have achieved great success in many areas including speech recognition. However, compared to LSTM models, the heavy computational cost of the Transformer during inference is a key issue to prevent their applications. In this work, we explored the potential of Transformer Transducer (T-T) models for the fist pass decoding with low latency and fast speed on a large-scale dataset. We combine the idea of Transformer-XL and chunk-wise streaming processing to design a streamable Transformer Transducer model. We demonstrate that T-T outperforms the hybrid model, RNN Transducer (RNN-T), and streamable Transformer attention-based encoder-decoder model in the streaming scenario. Furthermore, the runtime cost and latency can be optimized with a relatively small look-ahead.

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