Research On Whispering Speaker Recognition

Whispering speaker recognition is to recognize the speaker according to the speaker-related information in whispered speech, it can be applied in several fields, such as telephone banking, identification in special condition, the private speech in public, the special need for nation security, etc. It is such a new subject that many problems were left to fix.As the whispered speech is pronounced in different style from the normal speech, its performance in speaker recognition is quite different from normal speech too. This thesis describe the process of building an auto speaker recognition system based on Gaussian Mixture Model, this system was used to do the research on text independent speaker identification, and the difference of performance between whispered speech and normal speech is analyzed , an improvement to the whispering speaker recognition system was also made. The main work of this thesis is shown below:A whispered speech library and a normal speech library were recorded, pronounced by 22 people, the following features were used for speaker recognition of both normal speech and whispered speech: MFCC,LPCC,ΔMFCC,ΔLPCCand MFCC +LPCC.The performance of normal speaker recognition and whispering speaker recognition under different dimensions of MFCC was tested, the normal speech came to a best performance at the dimension 16, while the whispered had to use dimension 50 to reach the best performance.An improvement for MFCC is proposed: divide the whole frequency domain into several parts, and make the design of MFCC filters separately in each part. As such an improvement makes the design of each frequency band into independent process, the improved MFCC is more suitable for performing the small frequency characteristics of signal. The improved MFCC is effective to improve the performance of whispering speaker recognition, and this has been confirmed by experiments.