Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA

Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.

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Bibliographic Details
Main Authors: Phak Len, Eh Kan, Rafikha Aliana, Raof, Ahmad Nasir, Che Rosli, Razaidi, Hussin
Other Authors: phaklen@unimap.edu.my
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis 2009
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Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7326
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Institution: Universiti Malaysia Perlis
Language: English
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spelling my.unimap-73262009-11-18T03:06:54Z Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA Phak Len, Eh Kan Rafikha Aliana, Raof Ahmad Nasir, Che Rosli Razaidi, Hussin phaklen@unimap.edu.my Speech recognition Speech processing systems Automatic speech recognition Pattern perception Speaker recognition Speech processing Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. Feature extraction of speech is one of the most important issues in the field of speech recognition and representative of the speech. Mel Frequency Cepstral Coefficient (MFCC) is one the most important features required among various kinds of speech application. In this paper, FPGA-based for speech features extraction MFCC algorithm is proposed. The complexities of computational as well as requirement of memory usage are characterized, analyzed, and improved enormously. Look-up table scheme is used to deal with the elementary function value in the MFCC algorithm and fixed-point arithmetic is implemented to reduce the cost under accuracy study. The final feature extraction design is implemented effectively into the FPGA-Xilinx Virtex2 XC2V6000 FF1157-4 platform. 2009-11-18T03:06:54Z 2009-11-18T03:06:54Z 2009-10-11 Working Paper p.4A6 1 - 4A6 5 http://hdl.handle.net/123456789/7326 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009) Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Speech recognition
Speech processing systems
Automatic speech recognition
Pattern perception
Speaker recognition
Speech processing
spellingShingle Speech recognition
Speech processing systems
Automatic speech recognition
Pattern perception
Speaker recognition
Speech processing
Phak Len, Eh Kan
Rafikha Aliana, Raof
Ahmad Nasir, Che Rosli
Razaidi, Hussin
Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA
description Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
author2 phaklen@unimap.edu.my
author_facet phaklen@unimap.edu.my
Phak Len, Eh Kan
Rafikha Aliana, Raof
Ahmad Nasir, Che Rosli
Razaidi, Hussin
format Working Paper
author Phak Len, Eh Kan
Rafikha Aliana, Raof
Ahmad Nasir, Che Rosli
Razaidi, Hussin
author_sort Phak Len, Eh Kan
title Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA
title_short Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA
title_full Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA
title_fullStr Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA
title_full_unstemmed Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA
title_sort mel frequency cepstral coefficient (mfcc) extraction for speaker identification on fpga
publisher Universiti Malaysia Perlis
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7326
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