Significance of Frequency Band Selection of MFCC for Text-Independent Speaker Identification

被引：1

作者：

Dhonde, S. B. ^{[1
]}

Jagade, S. M. ^{[2
]}

机构：

[1] All India Shri Shivaji Mem Soc Inst Informat Tech, Dept Elect Engn, Pune 411001, Maharashtra, India

[2] TPCT COE, Dept Elect & Telecommun Engn, Osmanabad 413501, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA ENGINEERING AND COMMUNICATION TECHNOLOGY, ICDECT 2016, VOL 2 | 2017年 / 469卷

关键词：

Speaker recognition; Feature extraction; Mel scale; Vector quantization; MULTITAPER MFCC; FEATURES; VERIFICATION; WAVELET; RECOGNITION; TRANSFORMS;

D O I：

10.1007/978-981-10-1678-3_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents significance of Mel-frequency Cepstral Coefficients (MFCC) Frequency band selection for text-independent speaker identification. Recent studies have been focused on speaker specific information that may extends beyond telephonic passband. The selection of the frequency band is an important factor to effectively capture the speaker specific information present in the speech signal for speaker recognition. This paper focuses on development of a speaker identification system based on MFCC features which are modeled using vector quantization. Here, the frequency band is varied up to 7.75 kHz. Speaker identification experiments evaluated on TIMIT database consisting of 630 speaker shows that the average recognition rate achieved is 97.37 % in frequency band 0-4.85 kHz for 20 MFCC filters.

引用

页码：217 / 224

页数：8

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