Speaker Recognition Based on Long Short-Term Memory Networks
Qihang Xu, Mingjiang Wang, Changlai Xu, Lu Xu · 2020
Speaker recognition is the process of identifying the speaker's identity by extracting the acoustic features in the speaker's audio for recognition. It is mainly the perception and simulation of the speaker's vocal tract information and the human ear's auditory information, which has profound significance in the fields of human daily life and military affairs. In order to solve this problem, this paper proposes a speaker recognition algorithm based on Long Short-Term Memory Networks (LSTM). The feature uses a method that combines Linear Predictive Coding (LPC) with Log-Mel spectrum. The d-vector output through the LSTM network is classified using the Softmax loss function. This method is applied to the VCTK audio data set. The experimental results show that the recognition rate of this method reaches 94.9%.