Zhiqiang Liu
ABSTRACT
In the pseudo-label filtering for semi-supervised Mongolian speech recognition, the correctness of word combinations and the correctness of speech and word correspondences in the self-training set cannot be guaranteed simultaneously. To solve this problem, we propose a pseudo-label filtering strategy with fusion of perplexity and confidence, which is called sentence perplexity confidence. The strategy simultaneously evaluates the semantic relations of pseudo-labels and the correspondence between pseudo-labels and acoustic features of unlabeled speech, which improves the accuracy of the self-training set and thus the performance of the target speech recognition model output by semi-supervised training. We conducted ablation experiments and comparison experiments of sentence perplexity confidence on Mongolian datasets IMUT-MC and IMUT-MC-SMI. The experimental results show that the sentence perplexity confidence is ahead of the sentence-level confidence and perplexity in terms of accuracy improvement ability of the self-training set, and the output target speech recognition models reach 14.7% and 16.1% for WER and SER respectively.
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Index Terms
- SPCPFS: a pseudo-label filtering strategy with fusion of perplexity and confidence
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