On the use of the derivative of electroglottographic signals for characterization of nonpathological phonation

Henrich, Nathalie; d’Alessandro, Christophe; Doval, Boris; Castellengo, Michèle

doi:10.1121/1.1646401

Cited by 186 publications

(136 citation statements)

References 25 publications

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“…Glottal open and closed times were calculated from the EGG signal using a combination of peak-picking and thresholding on the high-pass filtered (10 Hz cutoff), differentiated EGG signal (Henrich, d'Alessandro, Doval, & Castellengo, 2004;Marasek & Pu¨tzer, 1997). Instants of glottal closure, which are typically the points of maximum positive gradient in each glottal period, were identified as peaks in the differentiated EGG signal.…”

Section: Egg Measuresmentioning

confidence: 99%

The effects of difficulty and gain versus loss on vocal physiology and acoustics

et al. 2007

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To examine the basis of emotional changes to the voice, physiological and electroglottal measures were combined with acoustic speech analysis of 30 men performing a computer task in which they lost or gained points under two levels of difficulty. Predictions of the main effects of difficulty and reward on the voice were not borne out by the data. Instead, vocal changes depended largely on interactions between gain versus loss and difficulty. The rate at which the vocal folds open and close (fundamental frequency; f 0 ) was higher for loss than for gain when difficulty was high, but not when difficulty was low. Electroglottal measures revealed that f 0 changes corresponded to shorter glottal open times for the loss conditions. Longer closed and shorter open phases were consistent with raised laryngeal tension in difficult loss conditions. Similarly, skin conductance indicated higher sympathetic arousal in loss than gain conditions, particularly when difficulty was high. The results provide evidence of the physiological basis of affective vocal responses, confirming the utility of measuring physiology and voice in the study of emotion.Descriptors: Emotional prosody, Voice, Electroglottography, Physiology, Difficulty, ValenceThe acoustic characteristics of natural, often involuntary markers of emotion in the voice and the physiological mechanisms responsible for such vocal modulation have as yet received little attention from researchers. In contrast to studies of acted emotional speech, the small number of studies on natural or induced emotional speech (e.g., Alpert, Kurtzberg, & Friedhoff, 1963;Bachorowski & Owren, 1995;Simonov & Frolov, 1973) have failed to identify acoustic patterns specific to different emotions. Instead, most acoustic changes measured in real or induced emotional speech can most parsimoniously be explained in terms of the general physiological arousal characteristic of different emotions. This contrasts with the theoretical view (e.g., Scherer, 1986) that emotions affect the acoustic characteristics of speech along multiple dimensions in a manner similar to the pattern of physiological changes that accompany emotion (e.g., Bradley, Codispoti, Cuthbert, & Lang, 2001;Cacioppo, Klein, Bernston, & Hatfield, 1993).The reasons for the paucity of research on real or elicited emotional speech have been described elsewhere (e.g., Johnstone, van Reekum, & Scherer, 2001;Scherer, Johnstone, & Klasmeyer, 2003). These include the practical difficulty of inducing emotions in the laboratory and the fact that whatever emotional information is carried in speech is often shrouded or masked by other aspects (e.g., linguistic, social, cultural) of the speech signal, as well as the relatively recent development of affordable and effective speech analysis software and hardware. One of the major impediments has been the difficulty of obtaining high-quality, controlled sound recordings of real emotions. A solution to this has been the use of computer tasks and games to induce emotional responses, or at least ...

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Section: Egg Measuresmentioning

confidence: 99%

The effects of difficulty and gain versus loss on vocal physiology and acoustics

et al. 2007

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“…4(b). Additionally, if an impulsive feature is spread in time then the group delay function method will find its center of energy, which is particularly useful in the case of the "redoubled" GCI discussed in [40]. A similar approach has been applied directly to speech signals [41] in which is not expected to take a constant value, nor whose mean is zero when the GCI lies in the center of the window.…”

Section: A Candidate Detectionmentioning

confidence: 99%

Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

Thomas

Guðnason

Naylor

2012

IEEE Trans. Audio Speech Lang. Process.

108

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Abstract-Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important for speech processing applications that benefit from glottal-synchronous processing including pitch tracking, prosodic speech modification, speech dereverberation, synthesis and study of pathological voice. We propose the Yet Another GCI/GOI Algorithm (YAGA) to detect GCIs from speech signals by employing multiscale analysis, the group delay function, and -best dynamic programming. A novel GOI detector based upon the consistency of the candidates' closed quotients relative to the estimated GCIs is also presented. Particular attention is paid to the precise definition of the glottal closed phase, which we define as the analysis interval that produces minimum deviation from an all-pole model of the speech signal with closedphase linear prediction (LP). A reference algorithm analyzing both electroglottograph (EGG) and speech signals is described for evaluation of the proposed speech-based algorithm. In addition to the development of a GCI/GOI detector, an important outcome of this work is in demonstrating that GOIs derived from the EGG signal are not necessarily well-suited to closed-phase LP analysis. Evaluation of YAGA against the APLAWD and SAM databases show that GCI identification rates of up to 99.3% can be achieved with an accuracy of 0.3 ms and GOI detection can be achieved equally reliably with an accuracy of 0.5 ms.Index Terms-Dynamic programming, electroglottograph (EGG), glottal closing instants (GCIs), glottal opening instants (GOIs), group delay function, multiscale analysis, speech processing.

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“…EGG has been used to identify laryngeal mechanisms [21]. Glottal closing and opening instants can be determined from the differentiated EGG signal, and hence glottal parameters such as fundamental frequency, open, closed or contact quotients can be derived [57]. The technique can be refined by the use of several electrodes [58,59] which can give access to larynx position and supra glottal contact such as ventricular-fold vibration.…”

Section: Electroglottography -Vocal Fold Contact Measurementmentioning

confidence: 99%

Analysing and Understanding the Singing Voice: Recent Progress and Open Questions

Kob¹,

Henrich²,

Herzel³

et al. 2011

CBIO

Self Cite

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Abstract:The breadth of expression in singing depends on fine control of physiology and acoustics. In this review, the basic concepts from speech acoustics, including the source-filter model, models of the glottal source and source-filter interactions, are described. The precise control, the extended pitch range, the timbre control and, in some cases, the uses of alternate phonation modes all merit further attention and explanation. Here we review features of the singing voice and the understanding that has been delivered by new measurement techniques. We describe the glottal mechanisms and the control of vocal tract resonances used in singing. We review linear and nonlinear components of the voice and the way in which they are measured and modelled and discuss the aero-acoustic models. We conclude with a list of open questions and active fields of research.

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On the use of the derivative of electroglottographic signals for characterization of nonpathological phonation

Cited by 186 publications

References 25 publications

The effects of difficulty and gain versus loss on vocal physiology and acoustics

The effects of difficulty and gain versus loss on vocal physiology and acoustics

Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

Analysing and Understanding the Singing Voice: Recent Progress and Open Questions

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