Conference Papers

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    Identification of Phonological Process: Final Consonant Deletion from Childrens' Speech
    (Institute of Electrical and Electronics Engineers Inc., 2018) Ramteke, P.B.; Supanekar, S.; Koolagudi, S.G.
    Children within the age range of 2 1/2 to 6 1/2 years face difficulties in pronunciation due to underdeveloped vocal tract and neuromotor control. They try to substitute a simple class of sounds in place of sounds difficult for them to pronounce. These pronunciation error patterns are called phonological processes. Phonological processes disappear as the child advances in age, and its analysis gives the measure of language learning ability of children over the time. Appearance of these processes after the specified age (8 years) represents a phonological disorder. In this paper, final consonant deletion, one of the phonological processes in the Kannada language is considered for the analysis. In final consonant deletion consonant, part syllable, syllable or part word which appear at the end of the word is deleted. As the part of the word is deleted, features efficient in speech recognition namely MFCCs and LPCCs are explored for the analysis. Dynamic time warping (DTW) algorithm is considered to compare the correct and mispronounced word for identification of the region of final consonant deletion. DTW comparison path is observed to warp around the end of the mispronounced word where the part of the word is deleted. Combination of 13 MFCCs and 13 LPCCs is observed to achieve the highest accuracy of 72.68% within the tolerance range of ±50ms. Results show that the features efficient in speech recognition are efficient in the identification of final consonant deletion. © 2018 IEEE.
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    Identification of Nasalization and Nasal Assimilation from Children’s Speech
    (Springer Science and Business Media Deutschland GmbH, 2020) Ramteke, P.B.; Supanekar, S.; Aithal, V.; Koolagudi, S.G.
    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. © 2020, Springer Nature Switzerland AG.