Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy
Automatic estimation of a speaker’s age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker’s age is presented. The method features a “divide and conquer” strategy wherein the speech data are divided into six groups based on the vowel...
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doaj-a0f0b780b367444384a320edab58df442020-11-25T02:30:16ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/534064534064Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion StrategySeyed Mostafa Mirhassani0Alireza Zourmand1Hua-Nong Ting2Biomedical Engineering Department, Faculty of Engineering, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, MalaysiaBiomedical Engineering Department, Faculty of Engineering, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, MalaysiaBiomedical Engineering Department, Faculty of Engineering, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, MalaysiaAutomatic estimation of a speaker’s age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker’s age is presented. The method features a “divide and conquer” strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier’s learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier’s outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier’s outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker’s age from various speech sources.http://dx.doi.org/10.1155/2014/534064 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seyed Mostafa Mirhassani Alireza Zourmand Hua-Nong Ting |
spellingShingle |
Seyed Mostafa Mirhassani Alireza Zourmand Hua-Nong Ting Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy The Scientific World Journal |
author_facet |
Seyed Mostafa Mirhassani Alireza Zourmand Hua-Nong Ting |
author_sort |
Seyed Mostafa Mirhassani |
title |
Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy |
title_short |
Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy |
title_full |
Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy |
title_fullStr |
Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy |
title_full_unstemmed |
Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy |
title_sort |
age estimation based on children’s voice: a fuzzy-based decision fusion strategy |
publisher |
Hindawi Limited |
series |
The Scientific World Journal |
issn |
2356-6140 1537-744X |
publishDate |
2014-01-01 |
description |
Automatic estimation of a speaker’s age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker’s age is presented. The method features a “divide and conquer” strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier’s learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier’s outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier’s outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker’s age from various speech sources. |
url |
http://dx.doi.org/10.1155/2014/534064 |
work_keys_str_mv |
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