Abstract—Speech processing has emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speaker recognition is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major concern. This paper presents an automatic Speaker Recognition model which is Text-Dependent where the speaker is allowed to speak only fixed text. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase, based on individual information (characteristics of voice) included in speech waves. Recognizer block employs MFCC (Mel Frequency Cepstrum Co-efficients) technique to get hybrid features for speaker identification/verification system. These features are used to train the ANN classifier in the training phase. Later in the testing phase the speaker is recognised based on the ANN classifier. The accuracy of 92% is achieved.

 

Index Terms—speaker recognition, MFCC, ANN classifier

Cite: S. S. Wali, S. M. Hatture, and S. Nandyal, "MFCC Based Text-Dependent Speaker Identification Using BPNN," International Journal of Signal Processing Systems, Vol. 3, No. 1, pp. 30-34, June 2015. doi: 10.12720/ijsps.3.1.30-34