Dysarthria is a set of congenital and traumatic neuromotor disorders that impair the physical production of speech. These impairments reduce or remove the normal control of the vocal articulators. The acoustic characteristics of dysarthric speech is very different from the speech signal collected from a normative population, with relatively larger intra-speaker inconsistencies in the temporal dynamics of the dysarthric speech [1] [2]. These inconsistencies result in poor audible quality for the dysarthric speech, and in low phone/speech recognition accuracy. Further, collecting and labeling the dysarthric speech is extremely difficult considering the small number of people with these disorders, and the difficulty in labeling the database due to the poor quality of the speech. Hence, it would be of great interest to explore on how to improve the efficiency of the acoustic models built on small dysarthric speech databases such as Nemours [3], or use speech databases collected from a normative population to build acoustic models for dysarthric speakers. In this work, we explore the latter approach.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kuruvachan K. George
    • 1
  • C. Santhosh Kumar
    • 1
  1. 1.Machine Intelligence Research Lab.Amrita School of EngineeringCoimbatoreIndia

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