Unsupervised Speech Recognition with N-Skipgram and Positional Unigram
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Liming Wang Mark Hasegawa-Johnson Chang D. Yoo
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.