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

• URL: http://arxiv.org/abs/2304.11053v1
• Date: Wed, 19 Apr 2023 18:09:27 GMT
• Title: A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale
• Authors: Cal Peyser, Michael Picheny, Kyunghyun Cho, Rohit Prabhavalkar, Ronny Huang, Tara Sainath
• Abstract summary: Unpaired text and audio injection have emerged as dominant methods for improving ASR performance in the absence of a large labeled corpus. 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. 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.
• Score: 64.10124092250126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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