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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References
Valoes, L.D.: The Importance of Language - Why Learning a Second Language is Important (2014). http://www.trinitydc.edu/continuing-education/2014/02/26/importance-of-language-why-learning-a-second-language-is-important/. Accessed 22 Nov 2015
Wantchinatimes: Number of Mandarin Chinese learners hits 100 million (2014). http://www.wantchinatimes.com/news-subclassnt.aspx?id=20140901000011&cid=1104%20/. Accessed 22 Nov 2015
Merritte, A.: Why learn a foreign language? Benefits of bilingualism (2013). http://www.telegraph.co.uk/education/educationopinion/10126883/Why-learn-a-foreign-language-Benefits-of-bilingualism.html. Accessed 22 Nov 2015
Berger, M.K.: Instrumental Assessment of Velopharyngeal Dysfunction: Multi-View Videofluoroscopy vs. Nasopharyngoscopy (n.d.). http://www.ohioslha.org/pdf/Convention/2011%20Handouts/SC18VoiceBergerC.pdf. Accessed 24 Nov 2015
Kummer, A.: Handout-Resonance-Disorders (2014). http://www.smiletrain.org/medical/for-patients/speech-services/Handout-Resonance-Disorders.pdf
Tsai, R: Teaching and learning the tones of Mandarin Chinese (2011). http://www.scilt.org.uk/portals/24/library/slr/issues/24/24_5_tsai.pdf. Accessed 21 Mar 2016
Zhang, F., Yin, P.: A study of pronunciation problems of English learners in China. Asian Soc. Sci. 5(6), 141–146 (2009)
Finegan, E., Rickford, J.R.: Language in USA: Themes for the Twenty First Century. Cambridge University Press, Cambridge (2004)
Lutter, M.: Mel-Frequency Cepstral Coefficients (2014). http://recognize-speech.com/feature-extraction/mfcc. Accessed 20 Mar 2016
Kaur, P., Singh, P., Garg, V.: Speech recognition system; challenges and techniques. Int. J. Comput. Sci. Inf. Technol. 3(3), 3989–3992 (Online)
Huang, X., Deng, L.: An overview of modern speech recognition. Accessed 26 Nov 2015
Lin, Y.C., Wang, H.C.: Nasal Detection in Continuous Mandarin Speech (n.d.). http://slam.iis.sinica.edu.tw/NGASR/paper/O-Cocosda2005-HCW.pdf. Accessed 26 Nov 2015
Schuller, B., Rigoll, G., Lang, M.: ‘Hidden Markov Model Based Speech Emotion Recognition’. In: IEEE ICASSP, pp. 1–3 (2003)
Rabiner, L.R., Juang, B.: Fundamentals of Speech Recognition, 2nd edn. Pearson Education Press, Singapore (2005)
Tiwari, V.: “MFCC and its applications in speaker recognition”. Deptartment of Electronics Engineering, Gyan Ganga Institute of Technology and Management, Bhopal, MP, India, (Received 5 Nov 2009, Accepted 10 Feb 2010)
Dhingra, S.D., Nijhawan, G.: Speech recognition using MFCC and DTW. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization), vol. 2, issue 8, August 2013. (Copyright to IJAREEIE)
Kong, J.: Speech Multi-Mode Research and Diversified Phonetics in Voice of China. Commercial Press, Beijing (2008)
Dang, J., Honda, K., Suzuki, H.: Morphological and acoustical analysis of the nasal and the paranasal cavities. J. Acoust. Soc. Amer. 96, 2088–2099 (1994)
Hawkins, S., Stevens, K.: Acoustic and perceptual correlates of the non nasal-nasal distinction of vowels. J. Acoust. Soc. Amer. 77, 1560–1575 (1985)
Gold, B., Morgan, N.: Speech and Audio Signal Processing. Wiley, New York (2000)
Becchetti, C., Ricotti, L.P.: Speech Recognition. Wiley, England (1999)
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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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