Study of harmonics-to-noise ratio and critical-band energy spectrum of speech as acoustic indicators of laryngeal and voice pathology

Abstract

Acoustic analysis of speech signals is a noninvasive technique that has been proved to be an effective tool for the objective support of vocal and voice disease screening. In the present study acoustic analysis of sustained vowels is considered. A simple k-means nearest neighbor classifier is designed to test the efficacy of a harmonics-to-noise ratio (HNR) measure and the critical-band energy spectrum of the voiced speech signal as tools for the detection of laryngeal pathologies. It groups the given voice signal sample into pathologic and normal. The voiced speech signal is decomposed into harmonic and noise components using an iterative signal extrapolation algorithm. The HNRs at four different frequency bands are estimated and used as features. Voiced speech is also filtered with 21 critical-bandpass filters that mimic the human auditory neurons. Normalized energies of these filter outputs are used as another set of features. The results obtained have shown that the HNR and the critical-band energy spectrum can be used to correlate laryngeal pathology and voice alteration, using previously classified voice samples. This method could be an additional acoustic indicator that supplements the clinical diagnostic features for voice evaluation.

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Index Terms

• Applied computing

  • Arts and humanities

    • Sound and music computing

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Speech recognition

• Hardware

  • Communication hardware, interfaces and storage

    • Signal processing systems

  • Robustness

    • Hardware reliability

      • Signal integrity and noise analysis