Unsupervised Speech Recognition with N-Skipgram and Positional Unigram Matching

• URL: http://arxiv.org/abs/2310.02382v1
• Date: Tue, 3 Oct 2023 19:05:32 GMT
• Title: Unsupervised Speech Recognition with N-Skipgram and Positional Unigram Matching
• Authors: Liming Wang, Mark Hasegawa-Johnson and Chang D. Yoo
• Abstract summary: We introduce a novel ASR system, ESPUM. This system harnesses the power of lower-order N-skipgrams (up to N=3) combined with positional unigram statistics gathered from a small batch of samples. Our model showcases competitive performance in ASR and phoneme segmentation tasks.
• Score: 67.98016412551245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training unsupervised speech recognition systems presents challenges due to GAN-associated instability, misalignment between speech and text, and significant memory demands. To tackle these challenges, we introduce a novel ASR system, ESPUM. This system harnesses the power of lower-order N-skipgrams (up to N=3) combined with positional unigram statistics gathered from a small batch of samples. Evaluated on the TIMIT benchmark, our model showcases competitive performance in ASR and phoneme segmentation tasks. Access our publicly available code at https://github.com/lwang114/GraphUnsupASR.

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