Evaluation of TTS Personification by GMM-Based Speaker Gender and Age Classifier

摘要

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.