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Combination of Long-Term and Short-Term Features for Age Identification from VoiceBUYUK, O. , ARSLAN, M. L.
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feature extraction, Gaussian mixture model, neural networks, speech processing, support vector machines
processing(20), speaker(19), speech(16), recognition(14), signal(13), language(12), deep(9), verification(8), neural(8), vector(7)
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About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 101 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02013
Web of Science Accession Number: 000434245000013
SCOPUS ID: 85047853422
In this paper, we propose to use Gaussian mixture model (GMM) supervectors in a feed-forward deep neural network (DNN) for age identification from voice. The GMM is trained with short-term mel-frequency cepstral coefficients (MFCC). The proposed GMM/DNN method is compared with a feed-forward DNN and a recurrent neural network (RNN) in which the MFCC features are directly used. We also make a comparison with the classical GMM and GMM/support vector machine (SVM) methods. Baseline results are obtained with a set of long-term features which are commonly used for age identification in previous studies. A feed-forward DNN and an SVM are trained using the long term features. All the systems are tested using a speech database which consists of 228 female and 156 male speakers. We define three age classes for each gender; young, adult and senior. In the experiments, the proposed GMM/DNN significantly outperforms all the other DNN types. Its performance is only comparable to the GMM/SVM method. On the other hand, experimental results show that age identification performance is significantly improved when the decisions of the short-term and long-term systems are combined together. We obtain approximately 4% absolute improvement with the combination compared to the best standalone system.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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