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Parametric cepstral analysis for pathological voice assessment

Published: 16 March 2008 Publication History

Abstract

Traditional methods to diagnose laryngeal pathologies such as laryngoscopy are considered invasive and uncomfortable. Methods based on acoustic analisys of speech signals have been investigated in order to diminish the number of laryngoscopical exams. Digital signal processing techniques have been used to perform an acoustic analysis for vocal quality assessment due to the simplicity and the non-invasive nature of the measurement procedures. Their employment is of special interest, as they can provide an objective diagnosis of pathological voices, and may be used as complementary tool in laryngoscopy. The degree of reliability and effectiveness of discriminating process of pathological voices from normal ones depends on the characteristics and parameters of voice used to train the employed classifier. This paper aims at evaluating the performance of the Linear Prediction Coding (LPC)-based cepstral analysis to discriminate pathological voices of speakers affected by vocal fold edema. For this purpose, LPC, cepstral, weighted cepstral, delta cepstral weighted delta cepstral mel-cepstral coefficients and are used. A vector-quantizing-trained distance classifier is used in the discrimination process.

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Cited By

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  • (2020)A Survey on Signal Processing Based Pathological Voice Detection TechniquesIEEE Access10.1109/ACCESS.2020.29852808(66749-66776)Online publication date: 2020
  • (2017)Optimized spectral floor in multi-band spectral subtraction for dysarthric speech recognition2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN.2017.8049953(245-250)Online publication date: Feb-2017
  • (2014)Detection and classification of voice pathology using feature selection2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA.2014.7073250(571-577)Online publication date: Nov-2014
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    cover image ACM Conferences
    SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
    March 2008
    2586 pages
    ISBN:9781595937537
    DOI:10.1145/1363686
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    Publication History

    Published: 16 March 2008

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    Author Tags

    1. cepstral analysis
    2. pathological voice assessment
    3. speech pathology
    4. speech processing

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    March 16 - 20, 2008
    Fortaleza, Ceara, Brazil

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    Cited By

    View all
    • (2020)A Survey on Signal Processing Based Pathological Voice Detection TechniquesIEEE Access10.1109/ACCESS.2020.29852808(66749-66776)Online publication date: 2020
    • (2017)Optimized spectral floor in multi-band spectral subtraction for dysarthric speech recognition2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN.2017.8049953(245-250)Online publication date: Feb-2017
    • (2014)Detection and classification of voice pathology using feature selection2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA.2014.7073250(571-577)Online publication date: Nov-2014
    • (2011)Automatic Arabic digit speech recognition and formant analysis for voicing disordered people2011 IEEE Symposium on Computers & Informatics10.1109/ISCI.2011.5959001(699-702)Online publication date: Mar-2011
    • (2011)Automatic voice disorder classification using vowel formantsProceedings of the 2011 IEEE International Conference on Multimedia and Expo10.1109/ICME.2011.6012187(1-6)Online publication date: 11-Jul-2011
    • (2011)Combining entropy measurements and cepstral analysis for pathological voice assessmentISSNIP Biosignals and Biorobotics Conference 201110.1109/BRC.2011.5740666(1-5)Online publication date: Jan-2011

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