Survey of Speaker Adaptation Methods in Speech Recognition

Speech is one of the ways of human-computer interaction, and speech recognition technology is an important part of artificial intelligence. In recent years, the application of neural network technology in the field of speech recognition has developed rapidly, and it has become the mainstream acoustic modeling technology in the field of speech recognition. However, there is a difference between target speaker's voice and training data in the test conditions, which leads to the problem of model incompatibility. Therefore, the speaker adaptation (SA) method is to solve the mismatch problem caused by the speaker difference, and the research on the speaker adaptation method has become a popular direction in the field of speech recognition. Compared with the speaker adaptation method in the traditional speech recognition system, the self-adaptation in the speech recognition system using neural network has the characteristics of huge model parameters and relatively small amount of data. Therefore, the speaker adaptation method in the neural network-based speech recognition system becomes a challenge. Firstly, this paper reviews the development history of the speaker adaptation method and the various problems encountered in the research of the neural network-based speaker adaptation method. Secondly, the speaker adaptation method is divided into the speaker adaptation method based on feature domain and the speaker adaptation method based on model domain. It also introduces the corresponding principles and improvement methods, and finally points out the pro-blems that still exist in the speaker adaptation method in speech recognition and the future development direction.