Improvements to harmonic model for extracting better speech features in clinical applications. (January 2018)

• Record Type: Journal Article
• Authors: Asgari, Meysam; Shafran, Izhak
• Is Part Of: Computer speech & language. Volume 47(2018)
• Page Start: 298
• Page End: 313
• Publication Date: 2018-01
• Subjects: Pitch tracking -- Voice activity detection -- Modified harmonic model; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.08.005
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Improvements on harmonic model leads to accurate estimation of voiced segments and pitch frequency in presence of background noise as well as clinical speech including inherent noises. Time-varying property of the proposed model allows more accurate estimation of amplitude variations (shimmer) in a short-term waveform. Acoustic features extracted using the proposed model are useful in clinical applications and experimental results on two clinical tasks show their advantages compare to standard acoustic features. Abstract: Acoustic properties of speech samples can provide important cues in the assessment of voice pathology and cognitive function. The goal of this study is to develop novel algorithms for robust and accurate estimation of speech features and employ them to build probabilistic speech models for characterizing and analyzing clinical speech. Toward this goal, we adopt a harmonic model (HM) of speech. We overcome certain drawbacks of this model and introduce an improved version of HM that leads us to accurate and reliable estimation of voiced segments, fundamental frequency, HNR, jitter, and shimmer. We evaluate the performance of our improved HM in the context of voicing detection and pitch estimation with other state-of-the-art techniques on the Keele data set. Through extensive experiments on several noisy conditions, we demonstrate that the proposed improvements provide substantial gains over other popular methods under different noise levels and environments. Next, we investigate the utility of developed measures on the speech-based assessment of cognitive impairments including clinical depression and autism spectrum disorder (ASD). Our preliminary results on two clinical tasks demonstrate the promise of our improved HM features in practical applications.