Abstract
The estimation of speaker age using vocal features is studied in this paper. Firstly, a large number of utterances from various speakers are collected for analysis. Secondly, the vocal features including prosodic and spectral features are extracted and compared among different age groups. The spectral energy ratios are proposed to effectively classify speakers from different age groups. Thirdly, artificial neural network is used to model the age features. Age model is learned for segment level classifiers and the probabilities of age distribution is used to generate effective features. Finally, age regression is implemented based on deep neural network. Experimental results show that the proposed model is effective, and the age features are robust to speaker variance.
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Mei, G., Min, X. (2018). Automatic Age Estimation Based on Vocal Cues and Deep Neural Network. In: Qiao, F., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2017. Advances in Intelligent Systems and Computing, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-65978-7_32
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DOI: https://doi.org/10.1007/978-3-319-65978-7_32
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