Open AccessAutomatic Speaker Identification by Voice Based on Vector Quantization Method
Author(s) -
N. Mamatov,
Samijonov Abdurashid,
Nurimov Parakhat,
Niyozmatova Nilufar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j9523.0881019
Subject(s) - vector quantization , mel frequency cepstrum , cluster analysis , speech recognition , computer science , speaker recognition , feature vector , pattern recognition (psychology) , speaker identification , feature extraction , artificial intelligence , quantization (signal processing) , linde–buzo–gray algorithm , speaker diarisation , feature (linguistics) , identification (biology) , algorithm , linguistics , philosophy , botany , biology
In this paper, the systems of speaker identification of a text-dependent and independent nature were considered. Feature extraction was performed using chalk-frequency cepstral coefficients (MFCC). The vector quantization method for the automatic identification of a person by voice has been investigated. Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Speakers were modeled using vector quantization (VQ). Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Codebooks of all announcers were collected in the database. From the results, it can be said that vector quantization using cepstral features produces good results for creating a voice recognition system.