Novel VTEO based Mel cepstral features for classification of normal and pathological voices

Hemant A. Patil, Pallavi N. Baljekar

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

In this paper, novel Variable length Teager Energy Operator (VTEO) based Mel cepstral features, viz., VTMFCC are proposed for automatic classification of normal and pathological voices. Experiments have been carried out using this proposed feature set, MFCC and their score-level fusion. Classification was performed using a 2 nd order polynomial classifier on a subset of the MEEI database. The equal error rate (EER) on fusion was 3.2% less than EER of MFCC alone which was used as the baseline. Effectiveness of the proposed feature-set was also investigated under degraded conditions using the NOISEX-92 database for babble and high frequency channel noise.

Original languageEnglish
Pages (from-to)509-512
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 01-12-2011
Externally publishedYes
Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
Duration: 27-08-201131-08-2011

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Error Rate
Fusion
Fusion reactions
Operator
Energy
Set theory
Baseline
Classifiers
Classifier
Polynomials
Polynomial
Subset
Experiment
Experiments
Voice
Length
Data Base

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Cite this

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abstract = "In this paper, novel Variable length Teager Energy Operator (VTEO) based Mel cepstral features, viz., VTMFCC are proposed for automatic classification of normal and pathological voices. Experiments have been carried out using this proposed feature set, MFCC and their score-level fusion. Classification was performed using a 2 nd order polynomial classifier on a subset of the MEEI database. The equal error rate (EER) on fusion was 3.2{\%} less than EER of MFCC alone which was used as the baseline. Effectiveness of the proposed feature-set was also investigated under degraded conditions using the NOISEX-92 database for babble and high frequency channel noise.",
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Novel VTEO based Mel cepstral features for classification of normal and pathological voices. / Patil, Hemant A.; Baljekar, Pallavi N.

In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 01.12.2011, p. 509-512.

Research output: Contribution to journalConference article

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