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arXiv · 2304.11053

A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale

Cal Peyser·Michael Picheny·Kyunghyun Cho·Rohit Prabhavalkar·Ronny Huang·Tara Sainath
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Abstract

Unpaired text and audio injection have emerged as dominant methods for improving ASR performance in the absence of a large labeled corpus. However, little guidance exists on deploying these methods to improve production ASR systems that are trained on very large supervised corpora and with realistic requirements like a constrained model size and CPU budget, streaming capability, and a rich lattice for rescoring and for downstream NLU tasks. In this work, we compare three state-of-the-art semi-supervised methods encompassing both unpaired text and audio as well as several of their combinations in a controlled setting using joint training. We find that in our setting these methods offer many improvements beyond raw WER, including substantial gains in tail-word WER, decoder computation during inference, and lattice density.

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Cal Peyser, Michael Picheny, Kyunghyun Cho, Rohit Prabhavalkar, Ronny Huang, Tara Sainath. 2023-04-19. A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale. https://arxiv.org/abs/2304.11053

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