Conformer-based Tibetan Speech Recognition Algorithm

This paper highlights a Tibetan speech recognition algorithm based on the Conformer model, aiming to improve communication efficiency in Tibetan-Chinese bilingual communities and promote the preservation and transmission of the Tibetan language. By combining convolutional neural networks and the self-attention mechanism, this algorithm effectively captures both local and global features of speech signals. Experiments conducted on a Tibetan dialect speech synthesis dataset show that the model's Word Error Rate (WER) can be reduced to 12.69%, indicating strong performance in Tibetan speech recognition tasks. The results provide new ideas for the development of Tibetan speech recognition technology and offer a pathway for future expansion of dialect data and model optimization.