Abstract
In this paper we present our study on detecting tone mispronunciations in Mandarin. Both template and HMM approaches are investigated. Schematic templates of pitch contours are shown to be impractical due to their larger pitch range of inter-, even intra-speaker variation. The statistical Hidden Markov Models (HMM) is used to generate a Goodness of Pronunciation (GOP) score for detection with an optimized threshold. To deal with the discontinuity issue of the F0 in speech, the multi-space distribution (MSD) modeling is used for building corresponding HMMs. Under an MSD-HMM framework, detection performance of different choices of features, HMM types and GOP measures are evaluated.
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Zhang, L., Huang, C., Chu, M., Soong, F., Zhang, X., Chen, Y. (2006). Automatic Detection of Tone Mispronunciation in Mandarin. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_61
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DOI: https://doi.org/10.1007/11939993_61
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