VQ Based Comparative Analysis of MFCC and LPC Speaker Recognition System

被引：0
|
作者：
Naveed, Iqra [1 ]
Saher, Farwa [2 ]
Ali, Muhammad Nadeem [3 ]
Farooq, Muhammad Sajid [3 ]
Hasan, Taimoor [3 ]
Iftikhar, Aqsa [3 ]
机构：
[1] Univ Management & Technol, Sch Engn, Lahore, Pakistan
[2] Bahauddin Zakariya Univ Multan, Dept Comp Sci, Multan, Pakistan
[3] Lahore Garrison Univ, Dept Comp Sci, Lahore, Pakistan
来源：
4TH INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING (IC)2 | 2021年
关键词：
speaker recognition; digital signal processing; feature extraction; vector-quantization; COMBINING EVIDENCE; WORD RECOGNITION; FEATURES; PREDICTION;
D O I：
10.1109/ICIC53490.2021.9693042
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Language is the foremost part of communication and speech is the main component of language. Speaker recognizers extract parameters from speech and characterize the speech signal for recognition. Speaker recognition is a significant part of digital signal processing but the speaker recognition system is greatly affected by noise. Noise degrades the performance of the system. With the rapid advancement of speech processing technology, the speaker recognition system has also improved to a great extent. Different feature extraction techniques have been proposed. This paper investigates the performance of the Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive coefficients (LPC) in speaker recognition using Vector Quantization (VQ) and proposed some optimization techniques for the best recognition system.
引用
收藏
页码：165 / 170
页数：6
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