Anzeige
Erschienen in:
2006 | OriginalPaper | Buchkapitel
Recognition Rate Prediction for Dysarthric Speech Disorder Via Speech Consistency Score
verfasst von : Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn Thubthong
Erschienen in: PRICAI 2006: Trends in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Dysarthria is a collection of motor speech disorder. A severity of dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called
speech consistency score (SCS)
. By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the
root mean square error
between the prediction rates and recognition rates are less than 7.0% (R
2
= 0.74) and 2.5% (R
2
= 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11 % (R
2
= 0.48) for HMM.