Emotional speech: towards a new generation of databases
Speech Communication - Special issue on speech and emotion
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
2005 Special Issue: Beyond emotion archetypes: Databases for emotion modelling using neural networks
Neural Networks - Special issue: Emotion and brain
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Objective and subjective evaluation of an expressive speech corpus
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
IEEE Transactions on Audio, Speech, and Language Processing
Hi-index | 0.00 |
This paper presents the validation of the expressive content of an acted corpus produced to be used in speech synthesis. The use of acted speech can be rather lacking in authenticity and therefore its expressiveness validation is required. The goal is to obtain an automatic classifier able to prune the bad utterances -with wrong expressiveness-. Firstly, a subjective test has been conducted with almost ten percent of the corpus utterances. Secondly, objective techniques have been carried out by means of automatic identification of emotions using different algorithms applied to statistical features computed over the speech prosody. The relationship between both evaluations is achieved by an attribute selection process guided by a metric that measures the matching between the misclassified utterances by the users and the automatic process. The experiments show that this approach can be useful to provide a subset of utterances with poor or wrong expressive content.