Improving the performance of Uyghur speech recognition based on Factorized Time-Delay Neural Network

Abstract

Speech recognition systems have low accuracy in recognizing the Uyghur language, a low-resource language, due to its strong language specificity and few public training datasets. Given this problem, considering the characteristics of Uyghur, we use morpheme units to build a language model and use mixture data augmentation methods to expand the training data. A 9-layer TDNN-F is applied, which can effectively utilize contextual information. An optimal 9.88% WER (Word Error Rate) is achieved in experiments on the open-source dataset THUYVG-20. Compared to the baseline system of this dataset, the WER is reduced by 6.7%, which significantly improves the accuracy of the Uyghur speech recognition, and provides a reference in other low-resource languages for speech recognization.