Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types. (July 2017)

• Record Type: Journal Article
• Authors: Liu, Dong; Kankare, Elina; Laukkanen, Anne-Maria; Alku, Paavo
• Is Part Of: Biomedical signal processing and control. Volume 36(2017)
• Page Start: 183
• Page End: 193
• Publication Date: 2017-07
• Subjects: Contact quotient (CQ) -- Normalized amplitude quotient (NAQ) -- Perceived firmness -- Voice training and therapy; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.04.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Eight EGG parameters and nine glottal flow parameters were compared for the same speech sample. CQ50% distinguished phonation types and correlated well with perception when F0 and SPL varied. NAQ predicted voice quality, when the effect of F0 and SPL had been excluded. Abstract: This study compared for the electroglottographic (EGG) signal how well six earlier presented and two new parameters distinguish between normal, breathy and pressed phonation and how well they correlate with perceptual evaluation. The results were compared with those obtained for nine parameters describing the glottal flow waveform obtained through inverse filtering of the acoustic speech pressure signal. Acoustic and dual-channel EGG signals were recorded for twenty female and twenty male subjects with healthy voices phonating sustained samples of the vowel [a:] in their habitual normal voice and in simulated breathy (hypofunctional) and pressed (hyperfunctional) phonation. The samples were perceptually evaluated by five voice specialists and rated for firmness of phonation. The best examples from 12 females and 12 males were used for the analyses. Few earlier studies have ranked the behavior of this many EGG and glottal flow parameters from this large speech data. Although the parameters differed in their ranking order, contact quotient calculated with a criterion level at 50% both from the EGG and the inverse filtered signal was strong in correlating with perception and in distinguishing phonation types in cases where fundamental frequency and sound pressure level also varied. When this variation was taken into account, the normalized amplitude quotient NAQ still had an effect in predicting voice quality. The results will have applicability in voice training and therapy and in development of machine learning -based classification methods.