Robust Speaker Recognition Algorithm
Wenchao Hao, Yi Chen, Lei Wang, Chunguang Li, Yueqin Feng, Qingyun Wang
Available Online October 2016.
- https://doi.org/10.2991/ceie-16.2017.19How to use a DOI?
- Speaker Recognition; Mel-Frequency Cepstral Coefficients; Gaussian Mixture Model
- The accuracy of speaker recognition algorithm would be decreased greatly due to the noise issues. According to noisy environment, a new robust speaker recognition algorithm is proposed in this paper. After Mel-frequency Cepstral Coefficient (MFCC) feature extraction, the features are calibrated with half rised-sine function. Then the features are processed by feature normalization, feature folding and feature mapping. A method of combining BP neural network(NN) with Gaussian mixture model(GMM) is proposed to improve the recognition accuracy and robustness of the model. The neural network works in the probability space of GMM gathers the interactive information between different speakers. The experiment result proves that the proposed algorithm shows more accuracy and robustness.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wenchao Hao AU - Yi Chen AU - Lei Wang AU - Chunguang Li AU - Yueqin Feng AU - Qingyun Wang PY - 2016/10 DA - 2016/10 TI - Robust Speaker Recognition Algorithm BT - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016) PB - Atlantis Press SP - 137 EP - 143 SN - 2352-5401 UR - https://doi.org/10.2991/ceie-16.2017.19 DO - https://doi.org/10.2991/ceie-16.2017.19 ID - Hao2016/10 ER -