DocumentCode
1798639
Title
Speaker identification under the changed sound environment
Author
Yanyan Shan ; Qi Zhu
Author_Institution
Coll. of Commun. & Inf. Eng., Nanjing Univ. of Posts & Telecommun., Nanjing, China
fYear
2014
fDate
7-9 July 2014
Firstpage
362
Lastpage
366
Abstract
Under the laboratory environment, speaker recognition has made great progress. But in real life, the performance of speaker recognition system is vulnerable to various factors, especially environmental noise and healthy condition. This paper studies the performance of speaker identification system when the tester suffers from the cold. The cold tends to induce inflammation and swelling of the nasal cavity, and then changes the modulation of nasals to sound source excitation signal and makes the speaker´s voice changed. R.G. Tull[8] also found the speaker recognition system´s performance significantly decreases when taking normal speech speaked by the healthy persons as train speech, while cold speech by persons who are catching cold as test speech. In this paper, through studying the composition of nasal and comparing the frequency domain properties of normal speech and cold speech, we find that the cold makes the low frequency components larger and high-frequency components smaller. So we propose the method using different pre-emphasis filter process normal speech and cold speech. Experimental results show that this method can improve the performance of the speaker identification system by 6% compared to general method all speeches are processed with the same filter.
Keywords
Gaussian processes; mixture models; speaker recognition; speech processing; Gaussian mixture model; changed sound environment; cold speech; frequency domain property; high-frequency component; inflammation; low frequency component; nasal cavity swelling; nasal modulation; pre-emphasis filter process; sound source excitation signal; speaker identification system; speaker recognition system; train speech; Band-pass filters; Cavity resonators; Digital filters; Speaker recognition; Speech; Speech processing; Speech recognition; Gaussian mixture model; cold speech; pre-emphasis filter; speaker identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Audio, Language and Image Processing (ICALIP), 2014 International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4799-3902-2
Type
conf
DOI
10.1109/ICALIP.2014.7009816
Filename
7009816
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