Towards End-to-end Unsupervised Speech Recognition

• URL: http://arxiv.org/abs/2204.02492v1
• Date: Tue, 5 Apr 2022 21:22:38 GMT
• Title: Towards End-to-end Unsupervised Speech Recognition
• Authors: Alexander H. Liu, Wei-Ning Hsu, Michael Auli, Alexei Baevski
• Abstract summary: We introduce wvu which does away with all audio-side pre-processing and improves accuracy through better architecture. In addition, we introduce an auxiliary self-supervised objective that ties model predictions back to the input. Experiments show that wvuimproves unsupervised recognition results across different languages while being conceptually simpler.
• Score: 120.4915001021405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised speech recognition has shown great potential to make Automatic Speech Recognition (ASR) systems accessible to every language. However, existing methods still heavily rely on hand-crafted pre-processing. Similar to the trend of making supervised speech recognition end-to-end, we introduce \wvu~which does away with all audio-side pre-processing and improves accuracy through better architecture. In addition, we introduce an auxiliary self-supervised objective that ties model predictions back to the input. Experiments show that \wvu~improves unsupervised recognition results across different languages while being conceptually simpler.

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