Towards models of phonation

Hanson, Helen M.; Stevens, Kenneth N.; Kuo, Hong-Kwang Jeff; Chen, Marilyn Y.; Slifka, Janet

doi:10.1006/jpho.2001.0146

Cited by 95 publications

(81 citation statements)

References 27 publications

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“…The second one was the energy in the frequency band 5-8 kHz. 8 A large amount of energy in this band can be a sign of noise due to an incomplete glottal closure [37].…”

Section: Analyzes Of F0 and Ltasmentioning

confidence: 99%

How Do Teachers With Self-Reported Voice Problems Differ From Their Peers With Self-Reported Voice Health?

2012

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ObjectivesThis randomized case-control study compares teachers with self-reported voice problems, to age, gender and school-matched colleagues with self-reported voice health. The self-assessed voice function is related to factors known to influence the voice: laryngeal findings, voice quality, and personality, psycho social and coping aspects, searching for causative factors of voice problems in teachers. MethodsSubjects and controls, recruited from a teacher-group in an earlier questionnaire study, underwent examinations of the larynx by High-Speed Imaging and Kymograms; voice recordings; Voice Range Profile; audiometry; self assessment of voice handicap and voice function; teaching and environmental aspects; personality; coping ; burnout, and work-related issues. The laryngeal and voice recordings were assessed by experienced phoniatricians and speech pathologists. ResultsThe subjects with self-assessed voice problems differed from their peers with self-assessed voice health by significantly longer recovery-time from voice problems, and scored higher on all subscales of the Voice Handicap Index-T. ConclusionsThe results show that the cause of voice dysfunction in this group of teachers with self-reported voice problems is not found in the vocal apparatus or within the individual. The individual's perception of a voice problem seems to be based on a combination of the number of symptoms and of how often the symptoms occur, along with the time for recovery. The results also underline the importance of using self-assessed reports of voice dysfunction 1 Background and aim

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“…The second one was the energy in the frequency band 5-8 kHz. 8 A large amount of energy in this band can be a sign of noise due to an incomplete glottal closure [37].…”

Section: Analyzes Of F0 and Ltasmentioning

confidence: 99%

How Do Teachers With Self-Reported Voice Problems Differ From Their Peers With Self-Reported Voice Health?

2012

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“…Changes in spectral tilt (the degree to which intensity drops off as frequency increases [28],) indicate changes in voice quality and can be quantified by comparing the amplitude levels of the first harmonic (H1) and first formant (F 1 ) or H1 and the second harmonic (H2) (e.g., [29,30]). Schroeder [3] determined spectral slope by ''splitting the spectrum in third-octave bands and fitting a line to their respective energies'' (p. 109) as an indication of harshness or softness.…”

Section: Voice Qualitymentioning

confidence: 99%

Expressive speech: Production, perception and application to speech synthesis

Erickson

2005

Acoust. Sci. & Tech.

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This paper reviews some of the recent issues and findings in the area of production and perception of expressive speech and the application to speech synthesis. Specifically, it discusses some of the current problems with data collection, labeling, techniques for analyzing voice quality and applying speech synthesis as an analysis tool. Directions for future work in order to improve synthesis of expressive speech are suggested along the lines of better modeling, labeling and voice quality analysis.

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“…The so-called  measure is based on the ratio between the sound energy above and below 1,000 Hz (e.g., Sundberg and Nordenberg, 2006). In their overview, Hanson et al (2001) discuss several further measures, each with some advantages and disadvantages in the context of various objectives (usually the detection of voice disorders or quantifying long-term average spectra). After experimenting with individual vowels produced by healthy voices, we decided to modify the existing measures (Volín and Zimmermann, 2011), as described below in the Method.…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics of Schwa in Czech Accented English

Volín

Weingartová²,

Skarnitzl³

2013

RiL

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The English central mid lax vowel (i.e., schwa) often contributes considerably to the sound differences between native and non-native speech. Many foreign speakers of English fail to reduce certain underlying vowels to schwa, which, on the suprasegmental level of description, affects the perceived rhythm of their speech. However, the problem of capturing quantitatively the differences between native and non-native schwa poses difficulties that, to this day, have been tackled only partially. We offer a technique of measurement in the acoustic domain that has not been probed properly as yet: the distribution of acoustic energy in the vowel spectrum. Our results show that spectral slope features measured in weak vowels discriminate between Czech and British speakers of English quite reliably. Moreover, the measurements of formant bandwidths turned out to be useful for the same task, albeit less direct.

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Towards models of phonation

Cited by 95 publications

References 27 publications

How Do Teachers With Self-Reported Voice Problems Differ From Their Peers With Self-Reported Voice Health?

How Do Teachers With Self-Reported Voice Problems Differ From Their Peers With Self-Reported Voice Health?

Expressive speech: Production, perception and application to speech synthesis

Spectral Characteristics of Schwa in Czech Accented English

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