Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm

Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task

Statistical parametric evaluation on new corpus design for Malay speech articulation disorder early diagnosis

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

Research Article Investigation of Effects of Different Synthesis Unit to the Quality of Malay Synthetic Speech

Abstract: Synthesis unit of a speech synthesizer directly affects the computational load and output speech quality. Generally, phoneme is the best choice to synthesize high quality speech. But it requires the knowledge of language to precisely draw the segmentation of words into phonemes. And it is expensive to compose an accurate phoneme dictionary. In this study, another type of synthesis unit is introduced which is letter. In Malay language, the unit size of letter is smaller than phoneme. And using letter as the synthesis unit could ease a lot of efforts because the context label can be created in fully automatic manner without the knowledge of the language. Four systems have been created and an investigation was done to find out how synthesis unit could affect the quality of synthetic speech. Forty eight listeners were hired to rate the output speech individually and result showed that no obvious difference between the output speech synthesized using different synthesis units. Listening test showed satisfactory result in terms of similarity, naturalness and intelligibility. Synthetic speech with polyphonic label showed increment in intelligibility compared to synthetic speech without polyphonic label. Using letter as the synthesis unit is recommended because it excludes the dependency of linguist and expands the idea of language independent front end text processing