With the development of deep learning and large models, the training of the model needs enough samples in speaker recognition, and when the training set is limited, it often fails to achieve good convergence. To solve the problem of a small training set, a speaker recognition method based on the combination of deep bidirectional gated recurrent unit neural network and SE block (Squeeze and Excitation block) is proposed. In this method, the deep bidirectional gated recurrent unit neural network mainly realizes the extraction of multiple information in different directions and depths of input speech, and then assigns different weights to the obtained information through SE-block, and finally uses the information to perform classification and recognition tasks. The experimental results show that the recognition accuracy reaches 90.34% when each speaker has only 6 trained speech, which shows that the model can achieve good results under a small number of training samples.
Key words
gated recurrent unit /
attention mechanism /
speaker recognition /
recurrent neural networks /
deep learning
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