An Efficient Streaming Non-Recurrent On-Device End-to-End Model with Improvements to Rare-Word Modeling
(3 minutes introduction)
|Tara N. Sainath (Google, USA), Yanzhang He (Google, USA), Arun Narayanan (Google, USA), Rami Botros (Google, USA), Ruoming Pang (Google, USA), David Rybach (Google, USA), Cyril Allauzen (Google, USA), Ehsan Variani (Google, USA), James Qin (Google, USA), Quoc-Nam Le-The (Google, USA), Shuo-Yiin Chang (Google, USA), Bo Li (Google, USA), Anmol Gulati (Google, USA), Jiahui Yu (Google, USA), Chung-Cheng Chiu (Google, USA), Diamantino Caseiro (Google, USA), Wei Li (Google, USA), Qiao Liang (Google, USA), Pat Rondon (Google, USA)|
On-device end-to-end (E2E) models have shown improvements over a conventional model on Search test sets in both quality, as measured by Word Error Rate (WER) , and latency , measured by the time the result is finalized after the user stops speaking. However, the E2E model is trained on a small fraction of audio-text pairs compared to the 100 billion text utterances that a conventional language model (LM) is trained with. Thus E2E models perform poorly on rare words and phrases. In this paper, building upon the two-pass streaming Cascaded Encoder E2E model , we explore using a Hybrid Autoregressive Transducer (HAT)  factorization to better integrate an on-device neural LM trained on text-only data. Furthermore, to further improve decoder latency we introduce a non-recurrent embedding decoder, in place of the typical LSTM decoder, into the Cascaded Encoder model. Overall, we present a streaming on-device model that incorporates an external neural LM and outperforms the conventional model in both search and rare-word quality, as well as latency, and is 318× smaller.