Modulation frequency and modulation level owing to vocal microtremor

Schoentgen, Jean

doi:10.1121/1.1492820

Cited by 18 publications

(34 citation statements)

References 15 publications

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“…As for modulation parameters, modulation extent has been reported to be much more significant for the perception of tremor than modulation rate [9,10]. This is coherent with the fact that physiologic and pathological tremors happen within overlapped frequency ranges [1,8,9]. Thus, amplitude serves better the purpose of discriminating between them.…”

Section: Discussionsupporting

confidence: 62%

“…The presence of such modulations by itself is not an indicator of dysphonia; instead, the presence of tremor is necessary for a natural sounding voice [2]. In fact, vocal tremor in healthy individuals (physiological tremor) has been measured, showing modulation rates below 5 Hz [8]. However, the modulation rate is not definite in discriminating between physiological and pathological voice tremors.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of tremulous voices using a biomechanical model

Fraile

Godino-Llorente

Kob

2015

J AUDIO SPEECH MUSIC PROC.

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Vocal tremor has been simulated using a high-dimensional discrete vocal fold model. Specifically, respiratory, phonatory, and articulatory tremors have been modeled as instabilities in six parameters of the model. Reported results are consistent with previous knowledge in that respiratory tremor mainly causes amplitude modulation of the voice signal while laryngeal tremor causes both amplitude and frequency modulation. In turn, articulatory tremor is commonly assumed to produce only amplitude modulations but the simulation results indicate that it also produces a high-frequency modulation of the output signal. Furthermore, articulatory tremor affects the frequency response of the vocal tract and it might thus be detected by analyzing the spectral envelope of the acoustic signal.

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Section: Discussionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Simulation of tremulous voices using a biomechanical model

Fraile

Godino-Llorente

Kob

2015

J AUDIO SPEECH MUSIC PROC.

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“…17, 40 Involuntary oscillatory changes in laryngeal muscles produce rhythmic alterations in vocal fold stiffness and fundamental frequency, and thus we describe the stiffness coefficient as K = a + b sin͑ T t͒. 35,39,41 Figures 6͑a͒ and 6͑b͒ show the time series and the corresponding spectrum of the model variable x 1 , respectively, where a = 1 and the frequency T and am- plitude b of vocal tremor are 5 Hz and 0.2, 10,41-43 respectively. Vocal tremor is characterized by the low-frequency modulations of phonatory frequency or speech cycle amplitude.…”

Section: A Modeling Abnormal Characteristics Of Parkinsonian Voicesmentioning

confidence: 99%

Studying vocal fold vibrations in Parkinson’s disease with a nonlinear model

Zhang

Jiang

Rahn

2005

Chaos: An Interdisciplinary Journal of Nonlinear Science

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A nonlinear model is applied to study pathologic vocal vibratory characteristics and voice treatments of Parkinson's disease. We find that a number of pathologic vocal characteristics commonly observed in Parkinson's disease, including reduced vibratory intensity, incomplete vocal closure, increased phonation threshold pressure, glottal tremor, subharmonics, and chaotic vocal fold vibrations, can be studied with this nonlinear model. We also find that two kinds of clinical voice treatments for Parkinson's disease, including respiratory effort treatment and Lee Silverman voice treatment can be studied with this computer model. Results suggest that respiratory effort treatment, in which subglottal pressure is increased, might aid in enhancing vibratory intensity, improving glottal closure, and avoiding vibratory irregularity. However, the Lee Silverman voice treatment, in which both subglottal pressure and vocal fold adduction are increased, might be better than respiratory effort treatment. Increasing vocal fold thickness would be further helpful to improve these pathologic characteristics. The model studies show consistencies with clinical observations. Computer models may be of value in understanding the dynamic mechanism of disordered voices and studying voice treatment effects in Parkinson's disease.

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“…Rabiner and Schafer state that peak clipping has the effect of flattening the spectral envelope of the signal [1, p.150]. This effect has been exploited by some researchers for the spectral analysis of voice signals [20].…”

Section: Appendix a Spectral Effect Of Signal Clipping Due To Micropmentioning

confidence: 99%

Analysis of Measured and Simulated Supraglottal Acoustic Waves

et al. 2016

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To date, while much attention has been paid to the estimation and modelling of the voice source (i.e. the glottal airflow volume velocity), the measurement and characterisation of the supraglottal pressure wave have been much less studied. Some previous results have unveiled that the supraglottal pressure wave has some spectral resonances similar to those of the voice pressure wave. This makes the supraglottal wave partially intelligible. While the explanation for such effect seems to be clearly related to the reflected pressure wave travelling upstream along the vocal tract, the influence that non-linear source-filter interaction has on it is not as clear. This paper provides an insight into this issue by comparing the acoustic analyses of measured and simulated supraglottal and voice waves. Simulations have been performed using a high-dimensional discrete vocal-fold model. Results of such comparative analysis indicate that spectral resonances in the supraglottal wave are mainly caused by the regressive pressure wave that travels upstream along the vocal tract and not by source-tract interaction. On the contrary and according to simulation results, source-tract interaction has a role in the loss of intelligibility that happens in the supraglottal wave with respect to the voice wave. This loss of intelligibility mainly corresponds to spectral differences around the frequency of the second formant.

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Modulation frequency and modulation level owing to vocal microtremor

Cited by 18 publications

References 15 publications

Simulation of tremulous voices using a biomechanical model

Simulation of tremulous voices using a biomechanical model

Studying vocal fold vibrations in Parkinson’s disease with a nonlinear model

Analysis of Measured and Simulated Supraglottal Acoustic Waves

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