A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages

Building an effective automatic speech recognition system typically requires a large amount of high-quality labeled data; However, this can be challenging for low-resource languages. Currently, self-supervised contrastive learning has shown promising results in low-resource automatic speech recognition, but there is no discussion on the quality of negative sample sets in speech contrastive learning. In this paper, we propose the false negatives impact elimination (FNIE) method to filter false negative samples and improve the quality of the negative sample set in speech. FNIE compares the support vector with the negative sample vector set and optimizes the corresponding loss function, allowing the model to learn better speech representations and achieve superior results in low-resource speech recognition. Experiments demonstrate that FNIE effectively filters negative samples, enhances the quality of the negative sample set, and improves the accuracy of speech recognition. The quality of the negative sample set significantly affects the model's learning ability, and using too many negative samples can deteriorate it. In a low-resource setting, our FNIE method achieved a relative improvement of 2.98% in WER on the English dataset, 14.3% in WER on the Uyghur dataset, and 4.04% in CER on the Mandarin dataset compared to the baseline model.