(June) Yuan Shangguan
June holds a Master’s in Computer Science from the University of Michigan, Ann Arbor. She transitioned from her PhD program to pursue industrial research in Automatic Speech Recognition (ASR) and low-latency, on-device speech and language models. At Google, she leads a team that has authored publications in ASR and Large Language Models (LLMs).
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The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate. Incremental speech recognizers output chunks of partially recognized words while the user is still talking. Partial results can be revised before the ASR finalizes its hypothesis, causing instability issues. We analyze the quality and stability of on-device streaming end-to-end (E2E) ASR models. We first introduce a novel set of metrics that quantify the instability at word and segment levels. We study the impact of several model training techniques that improve E2E model qualities but degrade model stability. We categorize the causes of instability and explore various solutions to mitigate them in a streaming E2E ASR system.
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A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Ruoming Pang
Antoine Bruguier
Wei Li
Raziel Alvarez
Zhifeng Chen
Chung-Cheng Chiu
David Garcia
Kevin Hu
Minho Jin
Qiao Liang
Cal Peyser
David Rybach
Yash Sheth
Mirkó Visontai
Yonghui Wu
Yu Zhang
Ding Zhao
ICASSP (2020)
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Thus far, end-to-end (E2E) models have not shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In this paper, we develop a first-pass Recurrent Neural Network Transducer (RNN-T) model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency. On the quality side, we incorporate a large number of utterances across varied domains to increase acoustic diversity and the vocabulary seen by the model. We also train with accented English speech to make the model more robust to different pronunciations. In addition, given the increased amount of training data, we explore a varied learning rate schedule. On the latency front, we explore using the end-of-sentence decision emitted by the RNN-T model to close the microphone, and also introduce various optimizations to improve the speed of LAS rescoring. Overall, we find that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional model. For example, for the same latency, RNN-T+LAS obtains a 8% relative improvement in WER, while being more than 400-times smaller in model size.
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STREAMING END-TO-END SPEECH RECOGNITION FOR MOBILE DEVICES
Raziel Alvarez
Ding Zhao
David Rybach
Yonghui Wu
Ruoming Pang
Qiao Liang
Deepti Bhatia
ICASSP (2019)
Preview abstract
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.
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