Comparison of Two Inverse Filtering Methods in Parameterization of the Glottal Closing Phase Characteristics in Different Phonation Types

Lehto, Laura; Airas, Matti; Björkner, Eva; Sundberg, Johan; Alku, Paavo

doi:10.1016/j.jvoice.2005.10.007

Cited by 18 publications

(13 citation statements)

References 33 publications

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“…It requires a manual setting of two input parameters: the number of concatenating segments to model the form of the vocal tract and the lip radiation factor. This algorithm showed a performance comparable to that of a well established manual method [Lehto et al, 2007]. A publically-available Matlab package called TKK Aparat by Matti Airas [2008] that implements IAIF offered us a platform for further development.…”

Section: Parametrisationmentioning

confidence: 85%

“…A number of publications dedicated to detection of pressed and breathy phonation modes (mostly in speech) employed descriptors derived from the glottal flow waveform such as amplitude quotient (AQ), normalised amplitude quotient (NAQ) and the difference between the first two harmonics (H1-H2) [Walker and Murphy 2007, Orr et al 2003, Drugman et al 2008, Lehto et al 2007, Sundberg et al 2004. These descriptors are considered particularly suitable for glottal flow waveform estimation because they are relatively robust to some estimation errors.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Breathy, Resonant, Pressed – Automatic Detection of Phonation Mode from Audio Recordings of Singing

Proutskova

Rhodes

Crawford

et al. 2013

Journal of New Music Research

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In this paper we present an experiment on automatic detection of phonation modes from recordings of sustained sung vowels. We created an open dataset specifically for this experiment, containing recordings of nine vowels from multiple languages, sung by a female singer on all pitches in her vocal range in phonation modes breathy, neutral, flow (resonant) and pressed. The dataset is available under a Creative Commons license at http://www.proutskova.de/phonation-modes/ .First, glottal flow waveform is estimated via inverse filtering (IAIF) from audio recordings. Then six parameters of the glottal flow waveform are calculated. A 4-class Support Vector Machine classifier is constructed to separate these features into phonation mode classes. We automated the IAIF approach by computing the values of the input arguments -lip radiation and formant count -leading to the best-performing SVM classifiers (average classification accuracy over 60%), yielding a physical model for the articulation of the vowels.We examine the steps needed to generalise and extend the experimental work presented in this paper in order to apply this method in ethnomusicological investigations.

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Section: Parametrisationmentioning

confidence: 85%

Section: Feature Extractionmentioning

confidence: 99%

Breathy, Resonant, Pressed – Automatic Detection of Phonation Mode from Audio Recordings of Singing

Proutskova

Rhodes

Crawford

et al. 2013

Journal of New Music Research

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show abstract

“…In this study, the following selection criteria were adopted: the best estimate was the one that showed the maximally flat closed phase for the glottal flow waveform and a minimal remaining formant ripple. These criteria have been widely used in previous glottal inverse filtering studies [e.g., [79][80][81]]. After (Adapted from [82]).…”

Section: Inverse Filteringmentioning

confidence: 99%

Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types

Liu

Kankare

Laukkanen

et al. 2017

Biomedical Signal Processing and Control

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a b s t r a c tThis 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.

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“…Glottal characteristics of laryngeal speech have been heavily studied [1,13,14,15,16]. Aerodynamic parameters calculated from the inverse-filtered waveforms such as open quotient (OQ), speed quotient (SQ), and area under the curve (AUC) have been used to conveniently describe the vibratory glottal cycle and quantify voice quality [12,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Aerodynamic parameters calculated from the inverse-filtered waveforms such as open quotient (OQ), speed quotient (SQ), and area under the curve (AUC) have been used to conveniently describe the vibratory glottal cycle and quantify voice quality [12,14,15]. OQ is the ratio between duration when the glottis is open and that of the entire vibratory cycle.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Neoglottal Vibration of Cantonese Tracheoesophageal Speech: Preliminary Aerodynamic Study Using Inverse Filtering

Chan

2012

Folia Phoniatr Logop

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Objective: Despite the many studies on tracheoesophageal (TE) speech, the actual vibratory characteristics of the neoglottis in TE speech are not known. The present study examined the neoglottal vibratory pattern of Cantonese TE speakers by comparison with the glottal waveform produced by laryngeal speakers. Subjects and Methods: Eight superior TE and 8 laryngeal speakers of Cantonese participated in the study. All speakers were instructed to produce the vowels /i, ᴂ, a, ᴐ, u/ 3 times, and both acoustic and aerodynamic signals were recorded. While the airflow signals were later inverse-filtered to obtain the neoglottal and glottal waveforms, the acoustic signals were analyzed to obtain vowel formants, which were used as inputs to inverse filtering. Aerodynamic parameters including open quotient (OQ), speed quotient (SQ), and area under the curve (AUC) were compared between TE and laryngeal speakers. -Results: Results revealed that TE speakers exhibited comparable OQ and AUC, but significantly reduced SQ when compared with laryngeal speakers. Conclusions: Both inverse-filtered airflow signals associated with TE and laryngeal speech of Cantonese appear to be triangular in shape, indicating the opening, closing and closed phases of the (neo)glottis. However, neoglottal waveforms are highly variable. Such variability and inconsistency in neoglottal waveforms might be related to the lack of complete control over the neoglottis in TE speakers.

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Comparison of Two Inverse Filtering Methods in Parameterization of the Glottal Closing Phase Characteristics in Different Phonation Types

Cited by 18 publications

References 33 publications

Breathy, Resonant, Pressed – Automatic Detection of Phonation Mode from Audio Recordings of Singing

Breathy, Resonant, Pressed – Automatic Detection of Phonation Mode from Audio Recordings of Singing

Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types

Analyzing Neoglottal Vibration of Cantonese Tracheoesophageal Speech: Preliminary Aerodynamic Study Using Inverse Filtering

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