This paper gives an overview of automatic speaker recognition technology for biometric authentication. A person can be identified by various characteristics such as signature, fingerprints, voice, facial features, etc. This type of authentication methods is known as biometric person authentication. Speaker recognition refers to the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. For a reliable and high accuracy of speech recognition, simple and efficient representation methods are required. In this paper, coefficients are extracted from incoming speech signal using MFCC and it represent trained vector of the speaker. Vector Quantization is the technique used for identification. To identify the speaker, the Euclidean distance between the acoustic vector of test input signal and the mapped codebook is calculated. The trained vector that produces the smallest Euclidean distance is identified as speaker.
Sreelakshmi V. : completed her B.Tech in Electronics & Communication Engineering under Mahatma Gandhi University. Currently she is pursuing M.Tech in Electronics with specialization in VLSI and Embedded System under Cochin University of Science and Technology (CUSAT).
Dr. Gnana Sheela K : received her Ph D in Electronics & S Communication from Anna University, Chennai. She is working as a Professor, Department of ECE at TocH Institute of Science and Technology. She has published 20 international journal papers. She is a life member of ISTE.
Mel Frequency Cepstrum Coefficients (MFCC)
Fast Fourier Transform (FFT)
Mel Filter Bank
Vector Quantization (VQ)
The automatic speaker recognition system consists of 2 phases: enrollment and testing phase. In the enrollment phase, a database of 8 speakers were created and stored in as a reference. The set of speaker’s voice samples are trained using MFCC and Vector Quantization. These feature vectors are stored as reference models. In the testing phase, the unknown speaker’s identity is matched against the reference models and the recognition is made. Speaker identification and verification are simulated and the results are verified. Simulation is completed using MATLAB 2013a. Speaker recognitions accuracy of 100% was obtained for a set of 8 pre-recorded speakers.
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