<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the development of the voice conversion and personification of the text-to-speech (TTS) systems, it is very necessary to have feedback information about the users’ opinion on the resulting synthetic speech quality. Therefore, the main aim of the experiments described in this paper was to find out whether the classifier based on Gaussian mixture models (GMM) could be applied for evaluation of different storytelling voices created by transformation of the sentences generated by the Czech and Slovak TTS system. We suppose that it is possible to combine this GMM-based statistical evaluation with the classical one in the form of listening tests or it can replace them. The results obtained in this way were in good correlation with the results of the conventional listening test, so they confirm practical usability of the developed GMM classifier. With the help of the performed analysis, the optimal setting of the initial parameters and the structure of the input feature set for recognition of the storytelling voices was finally determined.</p></abstract>

In the development of the voice conversion and personification of the text-to-speech (TTS) systems, it is very necessary to have feedback information about the users’ opinion on the resulting synthetic speech quality. Therefore, the main aim of the experiments described in this paper was to find out whether the classifier based on Gaussian mixture models (GMM) could be applied for evaluation of different storytelling voices created by transformation of the sentences generated by the Czech and Slovak TTS system. We suppose that it is possible to combine this GMM-based statistical evaluation with the classical one in the form of listening tests or it can replace them. The results obtained in this way were in good correlation with the results of the conventional listening test, so they confirm practical usability of the developed GMM classifier. With the help of the performed analysis, the optimal setting of the initial parameters and the structure of the input feature set for recognition of the storytelling voices was finally determined.

Storytelling Voice Conversion: Evaluation Experiment Using Gaussian Mixture Models

Department of Imaging Methods, Institute of Measurement Science, Slovak Academy of Sciences in Bratislava

Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology STU, Ilkovičova 3, SK-812 19 Bratislava

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In the development of the voice conversion and personification of the text-to-speech (TTS) systems, it is very necessary to have feedback information about the...

Storytelling Voice Conversion: Evaluation Experiment Using Gaussian Mixture Models

Online veröffentlicht: 19. Sept. 2015

Seitenbereich: 194 - 202

Eingereicht: 01. Dez. 2014

DOI: https://doi.org/10.2478/jee-2015-0032

Schlüsselwörter
storytelling voice conversion, spectral and prosodic features of speech, evaluation of speech quality, GMM classifier

© Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Storytelling Voice Conversion: Evaluation Experiment Using Gaussian Mixture Models

Online veröffentlicht: 19. Sept. 2015

Seitenbereich: 194 - 202

Eingereicht: 01. Dez. 2014

DOI: https://doi.org/10.2478/jee-2015-0032

Schlüsselwörterstorytelling voice conversion, spectral and prosodic features of speech, evaluation of speech quality, GMM classifier

© Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
storytelling voice conversion, spectral and prosodic features of speech, evaluation of speech quality, GMM classifier