Speaker recognition based on the improved double-threshold endpoint algorithm and multistage vector quantization
Jun Jie Zhu, Jingjing Zhang, Qiang Chen, Peipei Tu · 2017
The traditional double-threshold endpoint detection method has the phenomenon of missing detection. Therefore, the speech recognition (SR) system based on vector quantization (VQ) in this paper proposes an improved algorithm for this phenomenon, which effectively avoids the problem of missing detection. Then, Mel Frequency Cepstral Coefficients (MFCC) is used to extract the characteristic parameters of the speech signal, and the multistage vector quantization is used to quantify the characteristic parameters. Experimental results show that, the proposed algorithm improves the recognition rate of the text-independent speaker recognition system by 8.7%, and it also confirms that the longer the training speech is, the higher the recognition rate will be.