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Authors: Zakariya Qawaqneh ; Arafat Abu Mallouh and Buket D. Barkana

Affiliation: University of Bridgeport, United States

ISBN: 978-989-758-212-7

Keyword(s): Deep Neural Network, SDC, MFCCS, Speaker Age and Gender Classification.

Related Ontology Subjects/Areas/Topics: Acoustic Signal Processing ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Speech Recognition ; Telecommunications ; Theory and Methods

Abstract: Automatic speaker age and gender classification is an active research field due to the continuous and rapid development of applications related to humans’ life and health. In this paper, we propose a new method for speaker age and gender classification, which utilizes deep neural networks (DNNs) as feature extractor and classifier. The proposed method creates a model for each speaker. For each test speech utterance, the similarity between the test model and the speaker class models are compared. Two feature sets have been used: Mel-frequency cepstral coefficients (MFCCs) and shifted delta cepstral (SDC) coefficients. The proposed model by using the SDC feature set achieved better classification results than that of MFCCs. The experimental results showed that the proposed SDC speaker model + SDC class model outperformed all the other systems by achieving 57.21% overall classification accuracy.

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Paper citation in several formats:
Qawaqneh, Z.; Abu Mallouh, A. and Barkana, B. (2017). DNN-based Models for Speaker Age and Gender Classification.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 106-111. DOI: 10.5220/0006096401060111

@conference{biosignals17,
author={Zakariya Qawaqneh. and Arafat Abu Mallouh. and Buket D. Barkana.},
title={DNN-based Models for Speaker Age and Gender Classification},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={106-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006096401060111},
isbn={978-989-758-212-7},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - DNN-based Models for Speaker Age and Gender Classification
SN - 978-989-758-212-7
AU - Qawaqneh, Z.
AU - Abu Mallouh, A.
AU - Barkana, B.
PY - 2017
SP - 106
EP - 111
DO - 10.5220/0006096401060111

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