A robust speaker recognition system combining factor analysis techniques

2014 ◽  
Author(s):  
Shaghayegh Reza ◽  
Tahereh Emami Azadi ◽  
Jahanshah Kabudian ◽  
Yaser Shekofteh
Keyword(s):  
Factor Analysis ◽  
Speaker Recognition ◽  
Recognition System ◽  
Analysis Techniques ◽  
Robust Speaker Recognition

scholarly journals Cost-Sensitive Learning for Emotion Robust Speaker Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongdong Li ◽  
Yingchun Yang ◽  
Weihui Dai
Keyword(s):  
Speaker Recognition ◽  
Recognition System ◽  
Learning Technology ◽  
Speech Corpus ◽  
Voice Communication ◽  
Cost Sensitive Learning ◽  
Identification Rate ◽  
Telephone System ◽  
Robust Speaker Recognition ◽  
Voice Data

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Joint Factor Analysis of Channel Mismatch in Whispering Speaker Verification

2012 ◽  
Vol 37 (4) ◽  
pp. 555-559 ◽  
Author(s):  
Gang Lv ◽  
Heming Zhao
Keyword(s):  
Factor Analysis ◽  
Speaker Recognition ◽  
Speaker Verification ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Joint Factor ◽  
Channel Mismatch ◽  
Joint Factor Analysis ◽  
Correct Recognition Rate

Abstract A speaker recognition system based on joint factor analysis (JFA) is proposed to improve whispering speakers’ recognition rate under channel mismatch. The system estimated separately the eigenvoice and the eigenchannel before calculating the corresponding speaker and the channel factors. Finally, a channel-free speaker model was built to describe accurately a speaker using model compensation. The test results from the whispered speech databases obtained under eight different channels showed that the correct recognition rate of a recognition system based on JFA was higher than that of the Gaussian Mixture Model-Universal Background Model. In particular, the recognition rate in cellphone channel tests increased significantly.

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