Resumen
Recently, the performance of end-to-end speech recognition has been further improved based on the proposed Conformer framework, which has also been widely used in the field of speech recognition. However, the Conformer model is mostly applied to very widespread languages, such as Chinese and English, and rarely applied to speech recognition of Central and West Asian agglutinative languages. There are more network parameters in the Conformer end-to-end speech recognition model, so the structure of the model is complex, and it consumes more resources. At the same time, we found that there is a long-tail problem in Kazakh, i.e., the distribution of high-frequency words and low-frequency words is not uniform, which makes the recognition accuracy of the model low. For these reasons, we made the following improvements to the Conformer baseline model. First, we constructed a low-rank multi-head self-attention encoder and decoder using low-rank approximation decomposition to reduce the number of parameters of the multi-head self-attention module and model?s storage space. Second, to alleviate the long-tail problem in Kazakh, the original softmax function was replaced by a balanced softmax function in the Conformer model; Third, we use connectionist temporal classification (CTC) as an auxiliary task to speed up the model training and build a multi-task lightweight but efficient Conformer speech recognition model with hybrid CTC/Attention. To evaluate the effectiveness of the proposed model, we conduct experiments on the open-source Kazakh language dataset, during which no external language model is used, and the number of parameters is relatively compressed by 7.4% and the storage space is relatively reduced by 13.5 MB, while the training speed and word error rate remain basically unchanged.