Towards the search of detection in speech-relevant features for stress
Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond trying to relate the biometrical signature of voice with a possible neural activity that might generate alterations in voice produ...
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| Published in | Expert systems Vol. 32; no. 6; pp. 710 - 718 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.12.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-4720 1468-0394 |
| DOI | 10.1111/exsy.12109 |
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| Abstract | Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond trying to relate the biometrical signature of voice with a possible neural activity that might generate alterations in voice production. A total of 68, acoustical, glottal and biomechanical parameters were extracted from neutral and stressed speeches. The importance of the parameters was evaluated using t‐test, entropy, Receiver Operator Characteristic (ROC) and Wilcoxon methods and support vector machines algorithms for classification. The emotion under study is the stress produced when a speaker has to defend an idea opposite to his/her thoughts or feelings, and this stress is compared to self‐consistent speech. The results show tremor in the vocal folds to be the most relevant feature. |
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| AbstractList | Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond trying to relate the biometrical signature of voice with a possible neural activity that might generate alterations in voice production. A total of 68, acoustical, glottal and biomechanical parameters were extracted from neutral and stressed speeches. The importance of the parameters was evaluated using t-test, entropy, Receiver Operator Characteristic (ROC) and Wilcoxon methods and support vector machines algorithms for classification. The emotion under study is the stress produced when a speaker has to defend an idea opposite to his/her thoughts or feelings, and this stress is compared to self-consistent speech. The results show tremor in the vocal folds to be the most relevant feature. |
| Author | Rodellar-Biarge, Victoria Nieto-Lluis, Victor Gómez-Vilda, Pedro Palacios-Alonso, Daniel |
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(2006) Pattern Recognition and Machine Learning, Springer, Verlag. Gómez, P., V. Rodellar, V. Nieto, R. Martinez, A. Alvarez, B. Scola, C. Ramirez, D. Poletti and M. Fernández (2013) A system to monitor phonation quality in the clinics, The Fifth International Conference on eHealth, Telemedicine, and Social Medicine, (eTELEMED 2013), 253-258. Turk, M. and A.P. Pentland (1991) Eigen faces for recognition, Journal of Cognitive Neuroscience, 3, 71-86. Deller, J.R., J.G. Proakis and J.H.L. Hansen (1993) Discrete-Time Processing of Speech Signals MacMillan Pub, Co., Englewood Cliffs, NJ. Plutchick, R. (1994) The Psychology and Biology of Emotion, Harper Collins Publishers: New York. Chavhan, Y., M. L. Dohre and P. Yesaware (2010) Speech emotion recognition using support vector machine. International Journal of Computer Applications, vol. 1, pp. 6-9 . Fernandez, R. and R. 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| References_xml | – reference: Fernandez, R. and R. Picard (2011) Recognizing affect from speech prosody using hierarchical graphics models, Speech Communication, 53, 1088-405. – reference: Plutchick, R. (1994) The Psychology and Biology of Emotion, Harper Collins Publishers: New York. – reference: Zhou, G., J.H.L. Hansen and J.F. Kaiser (2001) Nonlinear feature based classification of speech under stress, IEEE Trans. on Speech and Audio Processing, 9, 201-216. – reference: Bishop, C. (2006) Pattern Recognition and Machine Learning, Springer, Verlag. – reference: Deller, J.R., J.G. Proakis and J.H.L. Hansen (1993) Discrete-Time Processing of Speech Signals MacMillan Pub, Co., Englewood Cliffs, NJ. – reference: Jollife, I. (1986) Principal Component Analysis, Springer Verlag, New York. – reference: Platt, J.C. (1999) Advances in kernel methods - support vector learning, in. Fast Training of Support Vector Machines Using Sequential Minimal Optimization, MIT Press: Cambridge, MA (US). – reference: Darwin, C. (1872) The Expression of the Emotions in Man and Animals. J. Murray: London. – reference: Origlia, A., A. Cotugno and V. Galata (2014) Continuous emotion recognition with phonetic syllables, speech emotion recognition with phonetics syllables, Speech Communication, 57, 155-169. – reference: Ramirez, J., P. Yelamos, J.M. Gorriz and J.C. Segura (2006) SVM-based speech endpoint detection using contextual speech features, Electronics Letters, 42, 426-428. – reference: Airas, M. and P. Alku (2006) Emotions in vowel segments of continuous speech: analysis of glottal flow using the normalized amplitude quotient, Phonetica, 63, 26-46. – reference: Turk, M. and A.P. Pentland (1991) Eigen faces for recognition, Journal of Cognitive Neuroscience, 3, 71-86. – reference: Burges, C.J. (1998) C.: a tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 121-167. – reference: Boslaugh, S. (2012) Statistics in a Nuttshel. Second edition, O'Reilly Media Inc. Editor, Sebastopol: Ca, USA. – reference: Gómez, P., V. Rodellar, V. Nieto, R. Martinez, A. Alvarez, B. Scola, C. Ramirez, D. Poletti and M. Fernández (2013) A system to monitor phonation quality in the clinics, The Fifth International Conference on eHealth, Telemedicine, and Social Medicine, (eTELEMED 2013), 253-258. – reference: Hastie, T., R. Tibshirani and J. Friedman (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Second Edition, Springer-Verlag: New York, pp. 580-581. – reference: Fay, M.P. and M.A. Proschan (2010) Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis test and multiple interpretations of decision rules, Statistics Surveys, 4, 1-39. – reference: Grichkovtsova, I., M. Morel and A. Lacheret (2012) The role of voice quality and prosodic contour in affective speech perception, Speech Communication, 54, 414-429. – reference: Hansen J. Kim H. L. and Rahurkar W, Ruzanski M. E. and Meyeshoff, J. (2011) Robust emotional stressed speech detection using weighted frequency subbands, EURASIP Journal on Advances in Signal Processing, Hindawi Publishing Corporation, doi: 10.1155/2011/9066789. – reference: Vapnik, V.N. (1998) Statistical Learning Theory, John Wiley and Sons Inc., New York. – reference: El Ayadi, M., M.S. Kamel and F. Karray (2011) Survey on speech emotion recognition: features, classification schemes and databases, Pattern Recognition, 44, 572-587. – reference: Fernandez, R. and R. Picard (2003) Modeling drivers' speech under stress, Speech Communication, 40, 145-159. – reference: Frank, M.G. and P. Ekman (2004) Appearing truthful generalizes across different deception situations, Journal of Personality and Social Psychology, 86, 486-495. – reference: Mittal, V. K. and B. Yegnanarayana (2013) Effect of glottal dynamics in the production of shouted speech, J. Acoust. Soc. Am, 133, pp. 3050-3061. – reference: Teager, H.M. (1980) Some observations on oral air flow during phonation, IEEE transactions on acoustics, Speech and Signal Processing, 28, 599-601. – reference: Moore, E.I., M.A. Clements, J.W. Peifer and L. Weisser (2008) Critical analysis of the impact of glottal features in the classification of clinical depression in speech, IEEE Trans. on Biomedical al Engineering, 55, 96-107. – reference: Benesty, J., M.M. Sondhi and Y. Huang (Eds)(2008) Handbook on Speech Processing, Springer, Berlin. – reference: Chavhan, Y., M. L. Dohre and P. Yesaware (2010) Speech emotion recognition using support vector machine. International Journal of Computer Applications, vol. 1, pp. 6-9 . – reference: Gómez, P., R. Fernández, V. Rodellar, V. Nieto, A. Álvarez, L.M. Mazaira, R. Martínez and J.I. Godino (2009) Glottal source biometrical signature for voice pathology detection, Speech Communication, 51, 759-781. – reference: Koolagudi, S. and K.S. Rao (2012) Emotion recognition from speech: a review, Int. J. Speech Tecnology, 15, 99-117. DOI:10.1007/s10772-011-9125-1. – reference: Teager, H.M. and S. Teager (1990) Evidence for nonlinear production mechanisms in the vocal tract, Speech Production and Speech Modeling, 55, 241-261. – reference: Waaramaa, T., A.M. Laukkanen, M. Airas and P. 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| SubjectTerms | affective computing Algorithms Analysis Biometrics emotion elicitation emotional tremor Emotions Entropy Expert systems feature extraction and selection glottal signature Pattern recognition Searching Signatures speaker's biometry Speech stress in speech Stresses Studies Voice |
| Title | Towards the search of detection in speech-relevant features for stress |
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