Development of gender and race recognition system using speech and recognition by using frequency spectrum
In this thesis, the development of an algorithm and system that is able to recognize gender and races by using the speech frequency spectrum is presented. Some of the features extracted are the formant frequency and fundamental frequency of speech signals. The formant frequencies are obtained by fi...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Learning Object |
Language: | English |
Published: |
School of Mechatronics Engineering
2009
|
Subjects: | |
Online Access: | http://dspace.unimap.edu.my/xmlui/handle/123456789/4600 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.unimap-4600 |
---|---|
record_format |
dspace |
spelling |
my.unimap-46002009-02-05T04:44:16Z Development of gender and race recognition system using speech and recognition by using frequency spectrum Ng Siew Fong Sazali Yaakob, Prof. Dr. (Advisor) Recognition system -- Design and construction Speech recognition Speech processing systems Automatic speech recognition Speech perception Linear prediction coefficient In this thesis, the development of an algorithm and system that is able to recognize gender and races by using the speech frequency spectrum is presented. Some of the features extracted are the formant frequency and fundamental frequency of speech signals. The formant frequencies are obtained by finding a set of predictor coefficient that minimizes the mean square error over a short segment of speech waveform. Whereas the fundamental frequency is estimated by finding a peak in the auto-correlation function with a corresponding delay. Results obtained using this approach to identify the gender by using formant frequency and fundamental frequency has proven to be practically applicable. The Back-Propagation Neural Network has been chosen for the classification purposes. These features will be fed into the neural network for training until it is able to outputs the appropriate gender and races. The speech recognition application is implemented using the Digital Signal Processor (DSP). The well trained network parameters were applied in the DSP by the new architecture support features that facilitate the development of efficient high level languages. Hence, C code was chosen to read a set of input features, as the weights are being adjusted and weights sum output from input features, the corresponding results are finally displayed on the Liquid Crystal Display (LCD) through DSP. 2009-02-05T04:44:15Z 2009-02-05T04:44:15Z 2008-05 Learning Object http://hdl.handle.net/123456789/4600 en School of Mechatronics Engineering |
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 |
Recognition system -- Design and construction Speech recognition Speech processing systems Automatic speech recognition Speech perception Linear prediction coefficient |
spellingShingle |
Recognition system -- Design and construction Speech recognition Speech processing systems Automatic speech recognition Speech perception Linear prediction coefficient Ng Siew Fong Development of gender and race recognition system using speech and recognition by using frequency spectrum |
description |
In this thesis, the development of an algorithm and system that is able to recognize gender and races by using the speech frequency spectrum is presented. Some of the features extracted are the formant frequency and fundamental frequency of speech
signals. The formant frequencies are obtained by finding a set of predictor coefficient that minimizes the mean square error over a short segment of speech waveform. Whereas the fundamental frequency is estimated by finding a peak in the auto-correlation function with a corresponding delay. Results obtained using this approach to identify the gender by using formant frequency and fundamental frequency has proven to be practically applicable. The Back-Propagation Neural Network has been chosen for the classification purposes. These features will be fed into the neural network for training until it is able to
outputs the appropriate gender and races. The speech recognition application is
implemented using the Digital Signal Processor (DSP). The well trained network
parameters were applied in the DSP by the new architecture support features that
facilitate the development of efficient high level languages. Hence, C code was chosen to
read a set of input features, as the weights are being adjusted and weights sum output
from input features, the corresponding results are finally displayed on the Liquid Crystal Display (LCD) through DSP. |
author2 |
Sazali Yaakob, Prof. Dr. (Advisor) |
author_facet |
Sazali Yaakob, Prof. Dr. (Advisor) Ng Siew Fong |
format |
Learning Object |
author |
Ng Siew Fong |
author_sort |
Ng Siew Fong |
title |
Development of gender and race recognition system using speech and recognition by using frequency spectrum |
title_short |
Development of gender and race recognition system using speech and recognition by using frequency spectrum |
title_full |
Development of gender and race recognition system using speech and recognition by using frequency spectrum |
title_fullStr |
Development of gender and race recognition system using speech and recognition by using frequency spectrum |
title_full_unstemmed |
Development of gender and race recognition system using speech and recognition by using frequency spectrum |
title_sort |
development of gender and race recognition system using speech and recognition by using frequency spectrum |
publisher |
School of Mechatronics Engineering |
publishDate |
2009 |
url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/4600 |
_version_ |
1643787963435319296 |
score |
13.214268 |