Vocal Fold Stiffness Estimates for Emotion Description in Speech

The present study affords emotional differentiation in speech from the behaviour of the biomechanical stiffness estimates in voice, regarding dispersion and cyclicality. The Glottal Cyclic Parameters are derived from the vibrato correlates found in the Glottal Source reconstructed from the phonated parts of speech and have been shown to be good indices to neurologic disease detection and monitoring. In this paper the application of these parameters to the characterization of the emotional states affecting a speaker when expressing truth opposite to when they believe not saying the truth is explored. The study is based on the reconstruction of the vocal fold stiffness parameters and in the detection of possible deviations induced by emotional tremor and stress from a baseline. The method is validated using results from the analysis of a gender-balanced speaker’s database. Normative values for the different parameters estimated are given and used in contrastive studies of some cases presented to discussion.

[1]  J. Horáček,et al.  Resonance properties of the vocal folds: in vivo laryngoscopic investigation of the externally excited laryngeal vibrations. , 2000, The Journal of the Acoustical Society of America.

[2]  María Victoria Rodellar Biarge,et al.  Glottal Source biometrical signature for voice pathology detection , 2009, Speech Commun..

[3]  María Victoria Rodellar Biarge,et al.  Voice Pathology Detection by Vocal Cord Biomechanical Parameter Estimation , 2005, NOLISP.

[4]  Paavo Alku,et al.  Emotions in Vowel Segments of Continuous Speech: Analysis of the Glottal Flow Using the Normalised Amplitude Quotient , 2006, Phonetica.

[5]  M. Hariharan,et al.  Human Affective (Emotion) behaviour analysis using speech signals: A review , 2012, 2012 International Conference on Biomedical Engineering (ICoBE).

[6]  L. F. Barrett,et al.  Handbook of Emotions , 1993 .

[7]  H. K. Schutte,et al.  Glottal flow through a two-mass model: comparison of Navier-Stokes solutions with simplified models. , 2002, Journal of the Acoustical Society of America.

[8]  Max A. Little,et al.  Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests , 2009, IEEE Transactions on Biomedical Engineering.

[9]  Eric F. Gardner,et al.  Summary Statement , 1946 .

[10]  David A. Berry,et al.  Mechanisms of modal and nonmodal phonation , 2001, J. Phonetics.

[11]  I. Cobeta,et al.  Acoustic voice analysis in patients with Parkinson's disease treated with dopaminergic drugs. , 1997, Journal of voice : official journal of the Voice Foundation.

[12]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[13]  María Victoria Rodellar Biarge,et al.  Neurological Disease Detection and Monitoring from Voice Production , 2011, NOLISP.

[14]  Elliot Moore,et al.  Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech , 2008, IEEE Transactions on Biomedical Engineering.

[15]  Max A. Little,et al.  Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests , 2009 .