Abstract
Since the technology is growing rapidly, a lot of people nowadays start to learn the foreign language by using computer or mobile phone where they can simply download the language learning software into their phone or computer, and learn it without attending the traditional class room. However, most of the language learning software on the market does not support the nasal recognition. If a user contains nasal voice, the system may not able to recognize and determine his/her voice. Thus, nasal user may find it difficult in using this kind of language learning system. In this research, a new Mandarin Language Learning System is developed for nasal voice user. This Mandarin Language Learning System able to understand the nasal pronunciation which allows the nasal voice user to learn Mandarin without facing any problems. Once the system able to recognize the nasal pronunciation, it will increase the accuracy of recognition and also the efficiency of the system. In this research, Mel Frequency Cepstral Coefficient (MFCC) features are extracted from nasal speech signal and normal voice signal. Later extracted signals are studied the difference and matching using Dynamic Time Warping (DTW) techniques. Results obtain are compared with Hidden Markov Model (HMM). The accuracy of Nasal Voice is much higher by Combining MFCC features and DTW.
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Muniandy, T., Alvar, T.A., Boon, C.J. (2017). Mandarin Language Learning System for Nasal Voice User. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2017. Lecture Notes in Computer Science(), vol 10645. Springer, Cham. https://doi.org/10.1007/978-3-319-70010-6_35
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DOI: https://doi.org/10.1007/978-3-319-70010-6_35
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