###### Storytelling Voice Conversion: Evaluation Experiment Using Gaussian Mixture Models

, EURASIP Journal on Advances in Signal Processing (2008), Article ID 258184. [21] SOUSA. R.—FERREIRA, A.—ALKU, P. : The Harmonic and Noise Information of the Glottal Pulses, Speech, Biomedical Signal Processing and Control 10 (2014), 137–143. [22] LECLERC, I.—DAJANI, H. R.—GIGUERE, C. : Differences in Shimmer Across Formant Regions, Journal of Voice 27 No. 6 (2013), 685–690. [23] PŘIBIL, J.—PŘIBILOVÁ, A.—ĎURAČKOVÁ, D. : Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances using the GMM Classifier, Journal of

###### EVALUATION OF SPECTRAL AND PROSODIC FEATURES OF SPEECH AFFECTED BY ORTHODONTIC APPLIANCES USING THE GMM CLASSIFIER

## Abstract

The paper describes our experiment with using the Gaussian mixture models (GMM) for classification of speech uttered by a person wearing orthodontic appliances. For the GMM classification, the input feature vectors comprise the basic and the complementary spectral properties as well as the supra-segmental parameters. Dependence of classification correctness on the number of the parameters in the input feature vector and on the computation complexity is also evaluated. In addition, an influence of the initial setting of the parameters for GMM training process was analyzed. Obtained recognition results are compared visually in the form of graphs as well as numerically in the form of tables and confusion matrices for tested sentences uttered using three configurations of orthodontic appliances.

###### Evaluation of speaker de-identification based on voice gender and age conversion

. Kleber, “Vocal aging effects on F0 and the first formant: A longitudinal analysis in adult speakers”, Speech Communication , 2010, 52, (7-8), 638–651. [17] C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer ,. [18] G. Muhammad and K. Alghathbar, “Environment recognition for digital audio forensics using MPEG-7 and mel cepstral features”, Journal of Electrical Engineering , 2011, 62, (4), 199–205. [19] J. Přibil and A. Přibilová, “GMM-based evaluation of emotional style transformation in Czech and Slovak”, Cognitive Computation

###### GMM-based speaker age and gender classification in Czech and Slovak

References 1] M. Li, K. J. Han and S. Narayanan, ”Automatic Speaker Age and Gender Recognition Using Acoustic and Prosodic Level In formation Fusion”, Computer Speech and Language, vol. 27, 2013, 151-167. [2] T. Bocklet, A. Maier, J. G. Bauer, F. Burkhardt and E. N¨oth, ”Age and Gender Recognition for Telephone Applications Based on GMM Supervectors and Support Vector Machines”, IEEE International Conference on Acoustics, Speech, and Signal Pro- cessing, 31 March - 4 April 2008, 1605-1608, Las Vegas, NV: IEEE

###### Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

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###### Speaker Identification Using Data-Driven Score Classification

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###### Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

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###### On the perturbed restricted three-body problem

\ddot{X}=\dfrac{\partial V}{\partial X}. \end{array}$$ (9) m Y ¨ = ∂ V ∂ Y . $$\begin{array}{} \displaystyle m\ddot{Y}=\dfrac{\partial V}{\partial Y}. \end{array}$$ (10) From Eq. ( 3 ), the potential V 1 between m and m 1 is given by V 1 = − G m m 1 [ 1 r 1 + A 1 + A 2 r 1 3 ] , $$\begin{array}{} \displaystyle V_{1}=-Gmm_1\Bigg[\dfrac{1}{r_1}+\dfrac{A_1+A}{2r_1^3}\Bigg], \end{array}$$ (11) and the potential V between m, m 1 and m 2 is also given by V 2 = − G m m 2 [ 1 r 2 + A 2 + A 2 r 2 3 ] , $$\begin{array}{} \displaystyle V_{2}=-Gmm_2\Bigg[\dfrac{1}{r