International Journal of Speech Technology

, Volume 19, Issue 3, pp 457–465 | Cite as

Performance of speaker identification using CSM and TM

  • R. Visalakshi
  • P. Dhanalakshmi


The main objective of this paper is to develop the system of speaker identification. Speaker identification is a technology that allows a computer to automatically identify the person who is speaking, based on the information received from speech signal. One of the most difficult problems in speaker recognition is dealing with noises. The performance of speaker recognition using close speaking microphone (CSM) is affected in background noises. To overcome this problem throat microphone (TM) which has a transducer held at the throat resulting in a clean signal and unaffected by background noises is used. Acoustic features namely linear prediction coefficients, linear prediction cepstral coefficients, Mel frequency cepstral coefficients and relative spectral transform-perceptual linear prediction are extracted. These features are classified using RBFNN and AANN and their performance is analyzed. A new method was proposed for identification of speakers in clean and noisy using combined CSM and TM. The identification performance of the combined system is increased than individual system due to complementary nature of CSM and TM.


Autoassociative neural network Radial basis function neural network Linear prediction coefficients Linear prediction cepstral coefficients Mel frequency cepstral coefficients Relative spectral transform perceptual linear prediction 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of CSEAnnamalai UniversityChidambaramIndia

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