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

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, 151-167.10.1016/j.csl.2012.01.008Search in Google Scholar

[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.10.1109/ICASSP.2008.4517932Search in Google Scholar

[3] G. Dobry, R. M. Hecht, M. Avigal and Y. Zigel, ”Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on Acoustic Speech Signal”, IEEE Transactions on Au- dio, Speech, and Language Processing, vol. 19, no. 7, 2011, 1975-1985.10.1109/TASL.2011.2104955Search in Google Scholar

[4] C. Van heerden, E. Barnard, M. Davel, C. Van der Walt, E. Van dyk, M. Feld and C. M¨uller, ”Combining Regression and Classification Methods for Improving Automatic Speaker Age Recognition”, IEEE International Conference on Acoustics, Speech, and Signal Processing, 14-19 March 2010, 5174-5177, Dallas, TX: IEEE.10.1109/ICASSP.2010.5495006Search in Google Scholar

[5] M. H. Bahari, M. Mclaren, H. Van Hamme and D. A. Van-Leeuwen, ”Speaker Age Estimation Using i-Vectors”, Engineering Applications of Artificial Intelligence, vol. 34, 2014, 99-108.10.1016/j.engappai.2014.05.003Search in Google Scholar

[6] M. Fairhurst, M. Erbilek and M. Da Costa-Abreu, ”Selective Review and Analysis of Aging Effects in Biometric System Implementation”, IEEE Transactions on Human-Machine Systems, vol. 45, no. 3, 2015, 294-303.10.1109/THMS.2014.2376874Search in Google Scholar

[7] N. Minematsu, M. Sekiguchi and K. Hirose, ”Automatic Estimation of One’s Age with His/her Speech Based upon Acoustic Modeling Techniques of Speakers”, IEEE International Con- ference on Acoustics, Speech, and Signal Processin, 13-17 May 2002, I-137-I-140, Orlando, FL, USA: IEEE.10.1109/ICASSP.2002.1005695Search in Google Scholar

[8] H. Meinedo and I. Trancoso, ”Age and Gender Classification using Fusion of Acoustic and Prosodic Features”, Interspeech 2010, 26-30 September 2010, Makuhari, Japan, 2822-2825.10.21437/Interspeech.2010-745Search in Google Scholar

[9] B. D. Barkana and J. Zhou, ”A new Pitch-Range Based Feature Set for a Speaker’s Age and Gender Classification”, Applied Acoustics, vol. 98, 2015, 52-61.10.1016/j.apacoust.2015.04.013Search in Google Scholar

[10] A. Fedorova, O. Glembek, T. Kinnunen and P. Matˇejka, ”Exploring ANN Back-Ends for i-Vector Based Speaker Age Estimation”, Interspeech 2015, 6-10 September 2015, Dresden, Germany, 3036-3040.10.21437/Interspeech.2015-103Search in Google Scholar

[11] D. Tihelka, M. Gr°uber and Z. Hanzl´ıˇcek, ”Robust Methodology for TTS Enhancement Evaluation”, Text, Speech and Dialogue, I. Habernal, V. Matouˇsek, 2013, LNAI 8082, Berlin Heidelberg, Springer, 442-449.10.1007/978-3-642-40585-3_56Search in Google Scholar

[12] J. Přibil, A. Přibilov´a and J. Matoušek, ”Experiment with GMM Based Artefact Localization in Czech Synthetic Speech”, Text, Speech, and Dialogue (TSD), P. Král, V. Matouˇsek, LNAI 9302, Springer, 2015, 23-31.10.1007/978-3-319-24033-6_3Search in Google Scholar

[13] D. A. Reynolds, R. C. Rose, ”Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models”, IEEE Transactions on Speech and Audio Processing, vol. 3, 1995, 72-83.10.1109/89.365379Search in Google Scholar

[14] A. Venturini, L. Zao and X. Coelho, ”On Speech Features Fusion, -Integration Gaussian Modeling and Multi-Style Training for Noise Robust Speaker Classification”, IEEE/ACM Transac- tions on Audio, Speech, and Language Processing, vol. 22, no. 12, 2014, 1951-1964.10.1109/TASLP.2014.2355821Search in Google Scholar

[15] M. Shah, C. Chakrabarti and A. Spanias, ”Within and Cross -Corpus Speech Emotion Recognition Using Latent Topic Model -Based Features”, EURASIP Journal on Audio, Speech, and Music Processing, 2015, vol. 4, 2015, 1-17.10.1186/s13636-014-0049-ySearch in Google Scholar

[16] J. Přibil, A. Přibilová and D. Ďuračková, ”Storytelling Voice Conversion: Evaluation experiment using Gaussian mixture models”, Journal of Electrical Engineering, vol. 66, 2015, 194-202, DOI: 10.1515/jee-2015-0032/.Search in Google Scholar

[17] J. Přibil and A. Přibilová, ”GMM-Based Evaluation of Emotional Style Transformation in Czech and Slovak”, Cognitive Computation, 2014, DOI: 10.1007/s12559-014-9283-y.10.1007/s12559-014-9283-ySearch in Google Scholar

[18] B. Božilović, B. Todorović and B. M. Obradović, ”Text-Independent Speaker Recognition Using Two-Dimensional Information Entropy”, Journal of Electrical Engineering, vol. 66, no. 3, 2015, 167-173.10.2478/jee-2015-0027Search in Google Scholar

[19] P. Boersma and D. Weenink, ”Praat: Doing Phonetics by Computer” (Version 5.4.22) [Computer Program], Retrieved 8 October 2015, from http://www.fon.hum.uva.nl/Praat.Search in Google Scholar

[20] I. T. Nabney, ”Netlab Pattern Analysis Toolbox”, Copyright (1996-2001), Retrieved February 16, 2012, from http://www.mathworks.com/matlabcentral/fileexchange/2654-netlab.Search in Google Scholar

[21] S. E. Linville, ”Source Characteristics of Aged Voice Assessed from Long-Term Average Spectra”, Journal of Voice, vol. 16, no. 4, 2002, 472-479.10.1016/S0892-1997(02)00122-4Search in Google Scholar

[22] R. J. Baken, ”The Aged Voice: A New Hypothesis”, Journal of Voice, vol. 19, no. 3, 2005, 317-325.10.1016/j.jvoice.2004.07.00516102660Search in Google Scholar

[23] J. D. Harnsberger, R. Shrivastav, W. S. Brown, H. Rothman and H. Hollien, ”Speaking Rate and Fundamental Frequency as Speech Cues to Perceived Age”, Journal of Voice, vol. 22, no. 1, 2008, 58-69.10.1016/j.jvoice.2006.07.00416968663Search in Google Scholar

[24] J. D. Harnsberger,W. S. Brown, R. Shrivastav and H. Rothman, ”Noise and Tremor in the Perception of Vocal Aging in Males”, Journal of Voice, vol. 24, no. 5, 2010, 523-530.10.1016/j.jvoice.2009.01.00319815378Search in Google Scholar

[25] G. Gosztolya and T. Grósz, ”Domain Adaptation of Deep Neural Networks for Automatic Speech Recognition via Wireless Sensors”, Journal of Electrical Engineering, vol. 67, no. 2, 2016, 124-130. 10.1515/jee-2016-0017Search in Google Scholar