DocumentCode
456451
Title
Talking Condition Identification using Circular Hidden Markov Models
Author
Shahin, Ismail
Author_Institution
Sharjah Univ.
Volume
1
fYear
0
fDate
0-0 0
Firstpage
1275
Lastpage
1280
Abstract
In this work, circular hidden Markov models (CHMMs) have been used to enhance the recognition performance of isolated-word speaker-dependent and text-dependent talking condition identification systems. Our talking conditions in this work are: neutral, shouted, loud, slow, and fast. Our results show that CHMMs significantly enhance the talking condition identification performance compared to the left-to-right hidden Markov models (LTRHMMs)
Keywords
hidden Markov models; speaker recognition; circular hidden Markov models; isolated-word speaker-dependent recognition; left-to-right hidden Markov models; text-dependent talking condition identification systems; Authentication; Automatic speech recognition; Hidden Markov models; Loudspeakers; Signal processing; Speaker recognition; Speech processing; Speech recognition; Testing; Text recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Communication Technologies, 2006. ICTTA '06. 2nd
Conference_Location
Damascus
Print_ISBN
0-7803-9521-2
Type
conf
DOI
10.1109/ICTTA.2006.1684562
Filename
1684562
Link To Document