Rethinking Evaluation in ASR: Are Our Models Robust Enough?

• URL: http://arxiv.org/abs/2010.11745v3
• Date: Sun, 2 May 2021 18:30:52 GMT
• Title: Rethinking Evaluation in ASR: Are Our Models Robust Enough?
• Authors: Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Paden Tomasello, Jacob Kahn, Gilad Avidov, Ronan Collobert, Gabriel Synnaeve
• Abstract summary: We show that, in general, reverberative and additive noise augmentation improves generalization performance across domains. We demonstrate that when a large enough set of benchmarks is used, average word error rate (WER) performance over them provides a good proxy for performance on real-world noisy data.
• Score: 30.114009549372923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single dataset. While the research community has coalesced around various benchmarks, we set out to understand generalization performance in acoustic modeling across datasets - in particular, if models trained on a single dataset transfer to other (possibly out-of-domain) datasets. We show that, in general, reverberative and additive noise augmentation improves generalization performance across domains. Further, we demonstrate that when a large enough set of benchmarks is used, average word error rate (WER) performance over them provides a good proxy for performance on real-world noisy data. Finally, we show that training a single acoustic model on the most widely-used datasets - combined - reaches competitive performance on both research and real-world benchmarks.

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