2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-1.3 | ||
| Paper Title | A Better and Faster End-to-End Model for Streaming ASR | ||
| Authors | Bo Li, Anmol Gulati, Jiahui Yu, Tara N. Sainath, Chung-Cheng Chiu, Arun Narayanan, Shuo-Yiin Chang, Ruoming Pang, Yanzhang He, James Qin, Wei Han, Qiao Liang, Yu Zhang, Trevor Strohman, Yonghui Wu, Google, United States | ||
| Session | SPE-1: Speech Recognition 1: Neural Transducer Models 1 | ||
| Location | Gather.Town | ||
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 | ||
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-LVCR] Large Vocabulary Continuous Recognition/Search | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1]] across many dimensions, including quality (as measured by word error rate (WER)) and endpointer latency [2]. However, the model still tends to delay the predictions towards the end and thus has much higher partial latency compared to a conventional ASR model. To address this issue, we look at encouraging the E2E model to emit words early, through an algorithm called FastEmit [3]. Naturally, improving on latency results in a quality degradation. To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR. Secondly, we also explore running a 2nd-pass beam search to improve quality. In order to ensure the 2nd-pass completes quickly, we explore non-causal Conformer layers that feed into the same 1st-pass RNN-T decoder, an algorithm we called Cascaded Encoders [5]. Overall, the Conformer RNN-T with Cascaded Encoders offers a better quality and latency tradeoff for streaming ASR. | ||