Abstract
This paper describes an experiment using the Gaussian mixture models (GMM)-based speaker gender and age classification for automatic evaluation of the achieved success in text-to-speech (TTS) system personification. The proposed two-level GMM classifier detects four age categories (child, young, adult, senior) as well as it discriminates gender for adult voices. This classifier is applied for gender/age estimation of the synthetic speech in Czech and Slovak languages produced by different TTS systems with several voices, using different speech inventories and speech modelling methods. The obtained results confirm the hypothesis that this type of classifier can be utilized as an alternative approach instead of the conventional listening test in the area of speech evaluation.
The work has been done in the framework of the COST Action IC 1206 (A. Přibilová), and was supported by the Czech Science Foundation GA16-04420S (J. Matoušek, J. Přibil), and the Grant Agency of the Slovak Academy of Sciences VEGA 2/0013/14 (J. Přibil).
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Přibil, J., Přibilová, A., Matoušek, J. (2016). Evaluation of TTS Personification by GMM-Based Speaker Gender and Age Classifier. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_35
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