Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition
This work addresses performance degradation in ASR due to noisy pseudo labels, but it is incremental as it builds on existing fine-tuning methods.
The paper tackles the problem of low-quality pseudo labels degrading performance in semi-supervised automatic speech recognition by proposing a filtering strategy based on probability scores, resulting in improved ASR performances on LibriSpeech.
Fine tuning self supervised pretrained models using pseudo labels can effectively improve speech recognition performance. But, low quality pseudo labels can misguide decision boundaries and degrade performance. We propose a simple yet effective strategy to filter low quality pseudo labels to alleviate this problem. Specifically, pseudo-labels are produced over the entire training set and filtered via average probability scores calculated from the model output. Subsequently, an optimal percentage of utterances with high probability scores are considered reliable training data with trustworthy labels. The model is iteratively updated to correct the unreliable pseudo labels to minimize the effect of noisy labels. The process above is repeated until unreliable pseudo abels have been adequately corrected. Extensive experiments on LibriSpeech show that these filtered samples enable the refined model to yield more correct predictions, leading to better ASR performances under various experimental settings.