Yajie Mao


2022

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SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale
Raphael Tang | Karun Kumar | Gefei Yang | Akshat Pandey | Yajie Mao | Vladislav Belyaev | Madhuri Emmadi | Craig Murray | Ferhan Ture | Jimmy Lin
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweight computation for inference. Of course, this impedes commercialization since most companies lack vast human and computational resources. In this paper, we explore training and deploying an ASR system in the label-scarce, compute-limited setting. To reduce human labor, we use a third-party ASR system as a weak supervision source, supplemented with labeling functions derived from implicit user feedback. To accelerate inference, we propose to route production-time queries across a pool of CUDA graphs of varying input lengths, the distribution of which best matches the traffic’s. Compared to our third-party ASR, we achieve a relative improvement in word-error rate of 8% and a speedup of 600%. Our system, called SpeechNet, currently serves 12 million queries per day on our voice-enabled smart television. To our knowledge, this is the first time a large-scale, Wav2vec-based deployment has been described in the academic literature.

2021

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Voice Query Auto Completion
Raphael Tang | Karun Kumar | Kendra Chalkley | Ji Xin | Liming Zhang | Wenyan Li | Gefei Yang | Yajie Mao | Junho Shin | Geoffrey Craig Murray | Jimmy Lin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don’t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.