Abstract
In the paper a pronunciation error detection method has been presented, wchich is based on word structural features. A lowcomplexity classifier has been proposed, that is not concentrated on a limited base of error patterns, but is flexible enough to find unspecified mispronunciations. Two classification variants using Dynamic Time Warping (DTW) algorithm has been tested on speech corpus containing recordings of 30 people.
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References
Zhao, T., Hoshino, A., Suzuki, M., Minematsu, N., Hirose, K.: Automatic chinese pronunciation error detection using svm trained with structural features. In: SLT. IEEE (2012)
Liang, M.S., Hung, J.Y., Lyu, R.Y., Chin Chiang, Y.: Pronunciation error detection for computer assisted pronunciation teaching in mandarin. In: 6th International Symposium on Chinese Spoken Language Processing, ISCSLP 2008, pp. 1–4 (2008)
Russell, M., Series, R.W., Wallace, J.L., Brown, C., Skilling, A.: The star system: an interactive pronunciation tutor for young children, pp. 161–175 (2000)
Demenko, G., Wagner, A., Cylwik, N.: The use of speech technology in foreign language pronunciation training. Archives of Acoustics 35(5), 309–330 (2010)
Demenko, G., Cylwik, N., Agnieszka, W.: Applying speech and language technology to foreign language education (2009)
Demenko, G., Wagner, A., Cylwik, N., Jokisch, O.: An audiovisual feedback system for acquiring l2 pronunciation and l2 prosody. In: SLaTE 2009 (2009)
Cylwik, N., Wagner, A., Demenko, G.: The EURONOUNCE corpus of non-native polish for ASR-based pronunciation tutoring system. In: SLaTE 2009 (2009)
Rosenberg, A., Colla, A.: A connected speech recognition system based on spotting diphone-like segments–preliminary results. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1987, vol. 12, pp. 85–88 (1987)
Saraclar, M., Nock, H.J., Khudanpur, S.: Pronunciation modeling by sharing gaussian densities across phonetic models, pp. 137–160 (2000)
Saraclar, M., Khudanpur, S.: Pronunciation change in conversational speech and its implications for automatic speech recognition, pp. 375–395 (2004)
Jokisch, O., Wagner, A., Sabo, R., Jaeckel, R., Cylwik, N., Rusko, M., Ronzhin, A., Hoffman, R.: Multilingual speech data collection for the assessment of pronunciation and prosody training in a language learning system. In: Proc. of Speech and Computer (SPECOM) (2009)
Xu, S., Jiang, J., Chen, Z., Xu, B.: Automatic pronunciation error detection based on linguistic knowledge and pronunciation space. In: ICASSP, pp. 4841–4844. IEEE (2009)
Ng, R.W.M., Hirose, K.: Syllable: A self-contained unit to model pronunciation variation. In: ICASSP, pp. 4457–4460. IEEE (2012)
Liang, M.S., Hong, Z.Y., Lyu, R.Y., Chiang, Y.C.: Data-driven approach to pronunciation error detection for computer assisted language teaching. In: Spector, J.M., Sampson, D.G., Okamoto, T., Kinshuk, C.S.A., Ueno, M., Kashihara, A. (eds.) ICALT, pp. 359–361. IEEE Computer Society (2007)
Strik, H., Truong, K.P., de Wet, F., Cucchiarini, C.: Comparing classifiers for pronunciation error detection. In: INTERSPEECH, pp. 1837–1840. ISCA (2007)
Witt, S.M.: Use of speech recognition in computer-assisted language learning (1999)
Witt, S.M., Young, S.J.: Phone-level pronunciation scoring and assessment for interactive language learning. Speech Communication 30(2-3), 95–108 (2000)
Kanters, S., Cucchiarini, C., Strik, H.: The goodness of pronunciation algorithm: a detailed performance study. In: SLaTE 2009 (2009)
Wang, Y.B., Shan Lee, L.: Improved approaches of modeling and detecting error patterns with empirical analysis for computer-aided pronunciation training. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5049–5052 (2012)
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Bugdol, M., Segiet, Z., Kręcichwost, M. (2014). Pronunciation Error Detection Using Dynamic Time Warping Algorithm. In: Piętka, E., Kawa, J., Wieclawek, W. (eds) Information Technologies in Biomedicine, Volume 4. Advances in Intelligent Systems and Computing, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-319-06596-0_32
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DOI: https://doi.org/10.1007/978-3-319-06596-0_32
Publisher Name: Springer, Cham
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