Identification of Palatal Fricative Fronting Using Shannon Entropy of Spectrogram

Pravin Bhaskar Ramteke, Sujata Supanekar, Venkataraja Aithal, Shashidhar G. Koolagudi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, an attempt has been made to identify palatal fricative fronting in children speech, where postalveolar /sh/ is mispronounced as dental /s/. In children’s speech, the concentration of energy (darkest part) of spectrogram for /s/ ranges 4000 Hz to 8000 Hz, whereas it ranges 3000 Hz 8000 Hz for /sh/. Gammatonegram follows the frequency subbands of the ear (wider for higher frequencies). Various spectral properties such as spectral centroid, spectral crest factor, spectral decrease, spectral flatness, spectral flux, spectral kurtosis, spectral spread, spectral skewness, spectral slope and Shannon entropy of the spectrogram (interval of 2000 Hz), extracted from the Gammatonegram are proposed for the characterization of /sh/ and /s/. The dataset recorded from 60 native Kannada speaking children of age between 3 1/2 to 6 1/2 years is considered for the analysis from NITK Kids’ Speech Corpus. Support vector machine (SVMs) is considered for the classification. Various combinations of the proposed features are considered for the evaluation, along with the MFCCs(39) and LPCCs(39). Combination of MFCCs(39), LPCCs(39) and Entropy(4) is observed to achieve highest mispronunciation identification performance of 83.2983%.

Original languageEnglish
Title of host publicationMining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings
EditorsP. B.R., Veena Thenkanidiyoor, Rajendra Prasath, Odelu Vanga
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030661861
Publication statusPublished - 2020
Event7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019 - Veling, India
Duration: 19-12-201922-12-2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11987 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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