Artefact Determination by GMM-Based Continuous Detection of Emotional Changes in Synthetic Speech
- Resource Type
- Conference
- Authors
- Pribil, Jiri; Pribilova, Anna; Matousek, Jindrich
- Source
- 2019 42nd International Conference on Telecommunications and Signal Processing (TSP) Telecommunications and Signal Processing (TSP), 2019 42nd International Conference on. :45-48 Jul, 2019
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Speech synthesis
Databases
Two dimensional displays
Training
Standards
Task analysis
GMM classification
statistical analysis
synthetic speech evaluation
text-to-speech system
- Language
The paper is focused on a description of a system for automatic detection of speech artefacts based on the Gaussian mixture model (GMM) classifier. The system enables to detect one or more artefacts in synthetic speech produced by a text-to-speech system. Our speech artefact detection uses continual GMM classification of emotional states in 2-D affective space of valence and arousal within the whole sentence and calculates the final change in the evaluated emotions. The detected shift to negative emotions indicates presence of an artefact in the analysed sentence. The basic experiments confirm functionality of the developed system producing results with sufficient correctness of artefact detection. These results are comparable to those attained by a standard listening test method. Additional investigations show relatively great influence of the number of mixtures, the number of used emotional classes, and types of speech features on the evaluated emotional shift.