Open AccessComparative study to realize an automatic speaker recognition system
Author(s) -
Fadwa Abakarim,
Abdenbi Abenaou
Publication year - 2022
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v12i1.pp376-382
Subject(s) - computer science , mel frequency cepstrum , speech recognition , speaker recognition , dimension (graph theory) , operator (biology) , pattern recognition (psychology) , artificial intelligence , fourier transform , compression (physics) , feature (linguistics) , feature extraction , mathematics , mathematical analysis , biochemistry , chemistry , materials science , linguistics , philosophy , repressor , transcription factor , pure mathematics , composite material , gene
In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.