Speech-to-Text or always been known as speech recognition plays an important role nowadays especially in medical area specifically in speech impairment. In this study, a Malay language speech-to-Text system was been designed by using Hidden Markov Model (HMM) as a statistical engine with emphasizing the way of Malay speech corpus design specifically for Malay articulation speech disorder. This study also describes and tests the correct number of state to analyze the changes in the performance of current Malay speech recognition in term of recognition accuracy. Statistical parametric representation method was utilized in this study and the Malay corpus database was constructed to be balanced with all the phonetic placed and manner of articulation sample appeared in Malay speech articulation therapy. The results were achieved by conducting few experiments by collecting sample from 80 patient speakers (child and adult) and contain for almost 30,720 sample training data.
This study aims to develop a computerized technique that uses speech recognition as a helping tool in speech therapy diagnosis for early detection. Somehow speech disorder can be divided into few categories which not all type will be fully covered in this research. This study only purposes a solving method for diagnosis of a patient that suffers from articulation disorder. Therefore a set of Malay language vocabulary has already been designed to tackle this issue where it will cover selected Malay consonants as a target words. Ten Malay target words had been choose to test the recognition accuracy of this system and the sample are taken from real patient from Hospital Sultanah Aminah (HSA: Speech therapist at Speech Therapy Center) where the hospital assists in the clinical trial. The result accuracy of the systems will help the Speech Therapist (ST) to give an early diagnosis analysis for the patient before next step can be purposed. An early percentage of correct sample achieved almost 50% in this experiment.
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