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.

本文提出了一种在标注稀缺，计算有限的情境下训练和部署自动语音识别系统的方法，使用第三方 ASR 系统和用户反馈标记函数作为弱监督来源，采用不同输入长度的 CUDA 图形池来加速推理，称为 SpeechNet 系统，在智能电视上实现了 Wav2vec 技术的大规模部署，获得了 8% 的词错误率相对改进和 600% 的加速。