Speech and Speaker Recognition Technology using MFCC and SVM

International Journal of Electronics and Communication Engineering
© 2015 by SSRG - IJECE Journal
Volume 2 Issue 5
Year of Publication : 2015
Authors : Anamika Baradiya and Vinay Jain
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How to Cite?

Anamika Baradiya and Vinay Jain, "Speech and Speaker Recognition Technology using MFCC and SVM," SSRG International Journal of Electronics and Communication Engineering, vol. 2,  no. 5, pp. 6-9, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I5P105

Abstract:

Speaker recognition is an active field of research with important forensic and security application .The investigation in the field of speaker recognition is in progress almost five decades and also there are several challenges and day to day new opportunities in this field. In observation of the fact that speech is the most natural form of communication for the human being it is also uses to express the sense and identity. A speaker is known through their tone which contained the information of speech signal. Speaker identification is one of the biometric identification technologies and now days it is use in different areas. The principle of Speaker recognition is to recognize the human being through their voice or speech signal. Speaker recognition is categorized into two categories such as speaker identification and speaker verification. The wider range of speaker recognition is in voice dialling, telephone shopping, telephone banking, database access services, voice mail and many others. Speaker features of the input speech from test subject will be extracted and matched against the speaker model. A probability will evaluate the similarity between the model and the measured observations. The common approach is based on a threshold set for the acoustic likelihood ratio to decide the test speaker is accepted or not. Conventional speaker verification systems use hidden Markov models (HMM) or Gaussian mixture model (GMM) to perform the likelihood ratio test [1-6]. These systems use a generative model for all speaker models. This will result in over-fitting and maybe cannot maximize the discrimination of speaker and impostors

Keywords:

Speaker verification, Speaker recognition, MFCC, SVM.

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