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

Affiliation: University of Bridgeport, United States

Keyword(s): Deep Neural Network, GMM-UBM, I-Vector, Speaker Age and Gender Classification, Fine-Tuning.

Abstract: Speakers’ age and gender classification is one of the most challenging problems in the field of speech processing. Recently, remarkable developments have been achieved in the neural network field, nowadays, deep neural network (DNN) is considered one of the state-of-art classifiers which have been successful in many speech applications. Motivated by DNN success, we jointly fine-tune two different DNNs to classify the speaker’s age and gender. The first DNN is trained to classify the speaker gender, while the second DNN is trained to classify the age of the speaker. Then, the two pre-trained DNNs are reused to tune a third DNN (AGender-Tuning) which can classify the age and gender of the speaker together. The results show an improvement in term of accuracy for the proposed work compared with the I-Vector and the GMM-UBM as baseline systems. Also, the performance of the proposed work is compared with other published works on a publicly available database.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Abu Mallouh, A., Qawaqneh, Z. and Barkana, B. D. (2017). Combining Two Different DNN Architectures for Classifying Speaker’s Age and Gender. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOSIGNALS; ISBN 978-989-758-212-7; ISSN 2184-4305, SciTePress, pages 112-117. DOI: 10.5220/0006096501120117

@conference{biosignals17,
author={Arafat {Abu Mallouh} and Zakariya Qawaqneh and Buket D. Barkana},
title={Combining Two Different DNN Architectures for Classifying Speaker’s Age and Gender},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOSIGNALS},
year={2017},
pages={112-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006096501120117},
isbn={978-989-758-212-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOSIGNALS
TI - Combining Two Different DNN Architectures for Classifying Speaker’s Age and Gender
SN - 978-989-758-212-7
IS - 2184-4305
AU - Abu Mallouh, A.
AU - Qawaqneh, Z.
AU - Barkana, B.
PY - 2017
SP - 112
EP - 117
DO - 10.5220/0006096501120117
PB - SciTePress

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