论文标题
滤镜和进化：半监督自动语音识别的渐进式伪标签
Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition
论文作者
论文摘要
使用伪标签进行微调自我监督预处理的模型可以有效地提高语音识别性能。但是，低质量的伪标签可能会误导决策边界并降低性能。我们提出了一种简单而有效的策略，以过滤低质量的伪标签以减轻此问题。具体而言，伪标签是在整个训练集中生产的，并通过根据模型输出计算出的平均概率得分过滤。随后，具有较高概率分数的话语的最佳百分比被认为是具有可信赖标签的可靠培训数据。该模型已迭代更新以校正不可靠的伪标签，以最大程度地减少嘈杂标签的效果。重复上述过程，直到对不可靠的伪碱进行了充分的校正。在LibrisPeech上进行的广泛实验表明，这些过滤样品使精制模型能够产生更正确的预测，从而在各种实验设置下提供更好的ASR性能。
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.
