Abstract
In children, nasalization is a commonly observed phonological process where the non-nasal sounds are substituted with nasal sounds. Here, an attempt has been made for the identification of nasalization and nasal assimilation. The properties of nasal sounds and nasalized voiced sounds are explored using MFCCs extracted from Hilbert envelope of the numerator of group delay (HNGD) Spectrum. HNGD Spectrum highlights the formants in the speech and extra nasal formant in the vicinity of first formant in nasalized voiced sounds. Features extracted from correctly pronounced and mispronounced words are compared using Dynamic Time Warping (DTW) algorithm. The nature of the deviation of DTW comparison path from its diagonal behavior is analyzed for the identification of mispronunciation. The combination of FFT based MFCCs and HNGD spectrum based MFCCs are observed to achieve highest accuracy of 82.22% within the tolerance range of ±50 ms.
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Acknowledgment
The authors would like to thank the Cognitive Science Research Initiative (CSRI), Department of Science & Technology, Government of India, Grant no. SR/CSRI/ 49/2015, for its financial support on this work.
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Ramteke, P.B., Supanekar, S., Aithal, V., Koolagudi, S.G. (2020). Identification of Nasalization and Nasal Assimilation from Children’s Speech. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_23
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