@inproceedings{1e94364756a1468aa2d81fa4f5eb18e4,
title = "Robust phoneme recognition for a speech therapy environment",
abstract = "Traditional speech therapy approaches for speech sound disorders have a lot of advantages to gain from computer-based therapy systems. In this paper, we propose a robust phoneme recognition solution for an interactive environment for speech therapy. With speech recognition techniques the motivation elements of computer-based therapy systems can be automated in order to get an interactive environment that motivates the therapy attendee towards better performances. The contribution of this paper is a robust phoneme recognition to control the feedback provided to the patient during a speech therapy session. We compare the results of hierarchical and flat classification, with naive Bayes, support vector machines and kernel density estimation on linear predictive coding coefficients and Mel-frequency cepstral coefficients.",
keywords = "Speech Therapy, Phoneme Detection, Kernel Density Estimation, Naive Bayes, Support Vector Machines",
author = "Sofia Cavaco and Jo{\~a}o Magalh{\~a}es and Andr{\'e} Grossinho and Isabel Guimar{\~a}es",
note = "Sem PDF.; 2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH) ; Conference date: 11-05-2016 Through 13-05-2016",
year = "2016",
language = "English",
series = " IEEE International Conference on Serious Games and Applications for Health",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "Proceedings of IEEE International Conference on Serious Games and Applications for Health (SeGAH)",
address = "United States",
}