A Survey on Speaker Recognition with Various Feature Extraction Techniques
DOI:
https://doi.org/10.26438/ijcse/v7i2.884887Keywords:
Speaker Recognition, Speaker identification and verification, vector quantization, Mel Frequency Cepstral CoefficientAbstract
Speech processing is one of the important application area of digital signal processing. For this purpose, speaker recognition is dominating today’s world. Speaker recognition is a process of speaker identification and speaker verification refers to specific tasks. Speaker recognition is the process of identifying a speaker by his/her speech samples. By extracting the speaker-specific features from the speech samples, the recognition task can be done. Speaker recognition technique is one of the most helpful recognition techniques in today world. It is very important to efficiently work without fail of Recognition system and identify correct person. Speaker recognition is to extract, characterize and recognize the information about speaker identity. This system involves many stages with multiple techniques for each. In this paper, the performance of Mel Frequency Cepstral Coefficient (MFCC), VQ vector quantization and Linear Prediction Coding (LPC) speaker recognition system using method. It is found that the MFCC is offer better recognition rate as contrasted to BFCC using VQ vector quantization as speaker modeling technique. The best technique in each stage makes the system more accurate and efficient.
References
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[4] Kinnunen T.and Kärkkäinen I., "Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification". Joint IAPR Int. Workshop on Statistical Pattern Recognition (SPR`2002), Windsor, Canada, 681-688, August 2002.
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