Recognition rate prediction for dysarthric speech disorder via speech consistency score

Authors:
Prakasith Kayasith;Thanaruk Theeramunkong;Nuttakorn Thubthong
Affiliations:
Assistive Techn. Center, National Electronics and Computer Techn. Center, Klong Luang, Pathumthani, Thailand and School of Inf. and Computer Techn., Sirindhorn International Inst. of Techn., Thamm ...;School of Information and Computer Technology, Sirindhorn International Institute of Technology, Thammasat University, Klong Luang, Pathumthani, Thailand;Acoustics and Speech Research Laboratory, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 0
Cited 2

Incorporated speech overlapped factor (Φ) into speech clarity index (Ψ): method to improve dysarthric speech severity evaluation

Proceedings of the 1st international convention on Rehabilitation engineering & assistive technology: in conjunction with 1st Tan Tock Seng Hospital Neurorehabilitation Meeting
Speech confusion index (Φ): A confusion-based speech quality indicator and recognition rate prediction for dysarthria

Computers & Mathematics with Applications

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Abstract

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% (R2 = 0.74) and 2.5% (R2 = 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% (R2 = 0.48) for HMM.