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Erschienen in:
Experimental Analysis on Big Data in IOT-Based Architecture Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

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

verfasst von : S. B. Dhonde, S. M. Jagade

Erschienen in: Proceedings of the International Conference on Data Engineering and Communication Technology

Verlag: Springer Singapore

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Abstract

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.

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Vorheriges Kapitel A New Approach to Determine Tie-Line Frequency Bias (B) in Interconnected Power System with Integral Control AGC Scheme
Nächstes Kapitel Ensuring Performance of Graphics Processing Units: A Programmer’s Perspective
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Metadaten
Titel
Significance of Frequency Band Selection of MFCC for Text-Independent Speaker Identification
verfasst von
S. B. Dhonde
S. M. Jagade
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of the International Conference on Data Engineering and Communication Technology

Print ISBN: 978-981-10-1677-6

Electronic ISBN: 978-981-10-1678-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-1678-3_21