Emotion Recognition based on Phoneme Classes
Abstract
Recognizing human emotions/attitudes from speech cues has gained increased attention recently. Most previous work has focused primarily on suprasegmental prosodic features calcu-lated at the utterance level for modeling against details at the segmental phoneme level. Based on the hypothesis that dif-ferent emotions have varying effects on the properties of the different speech sounds, this paper investigates the usefulness of phoneme-level modeling for the classification of emotional states from speech. Hidden Markov models (HMM) based on short-term spectral features are used for this purpose using data obtained from a recording of an actress ’ expressing 4 different emotional states- anger, happiness, neutral, and sadness. We designed and compared two sets of HMM classifiers: a generic set of “emotional speech ” HMMs (one for each emotion) and a set of broad phonetic-class based HMMs for each emotion type considered. Five broad phonetic classes were used to explore the effect of emotional coloring on different phoneme classes, and it was found that spectral properties of vowel sounds were the best indicator of emotions in terms of the classification per-formance. The experiments also showed that the better per-formance can be obtained by using phoneme-class classifiers than generic “emotional ” HMM classifier and classifiers based on global prosodic features. To see the complementary effect of the prosodic and spectral features, the two classifiers were combined at the decis…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SVC was used for emotion recognition in the authors' previous study, showing better performance than other statistical classifiers.
“SVC was used for emotion recognition in our previous study, showing better performance than other statistical classifiers [13][14].”
From Analysis of emotion recognition using facial expressions, speech and multimodal information (2004) · cites this paper as [14]
Cites
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
Emotion recognition in human-computer interaction
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.911197 · Cited by 2,603 · see citing works
Acoustic profiles in vocal emotion expression.
Banse, R., & Scherer, K. R. (1996) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.70.3.614 · Cited by 2,253 · see citing works
Vocal communication of emotion: A review of research paradigms
Scherer, K. (2003) · Speech Communication · doi:10.1016/s0167-6393(02)00084-5 · Cited by 2,044 · see citing works
Speech emotion recognition using hidden Markov models
Nwe, T. L., Foo, S. W., & De Silva, L. C. (2003) · Speech Communication · doi:10.1016/s0167-6393(03)00099-2 · Cited by 920 · see citing works
Toward detecting emotions in spoken dialogs
Lee, C. M., & Narayanan, S. S. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.838534 · Cited by 655 · see citing works
Self-citationProsody-based automatic detection of annoyance and frustration in human-computer dialog
Ang, J., Dhillon, R., Krupski, A., Shriberg, E. E., & Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-559 · Cited by 390 · see citing works
Speech emotion recognition using hidden Markov models
Rodríguez, A. N., Moreno, A., Bonafonte, A., & Acebal, J. B. M. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-627 · Cited by 235 · see citing works
An acoustic study of emotions expressed in speech
Yıldırım, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Deng, Z., Lee, S., Narayanan, S. S., & Busso, C. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-242 · Cited by 172 · see citing works
Self-citationEmotional space improves emotion recognition
Tato, R., Santos, R. M., Kompe, R., & Pardo, J. M. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-557 · Cited by 159 · see citing works
Recognition of negative emotions from the speech signal
Lee, C. M., Narayanan, S. S., & Pieraccini, R. (2005) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2001.1034632 · Cited by 147 · see citing works
Self-citationShafran, I., Riley, M., & Mohri, M. (2004) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2003.1318399 · Cited by 112 · see citing works
Feature analysis and neural network-based classification of speech under stress
Hansen, J. H. L., & Womack, B. D. (1996) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.506935 · Cited by 98 · see citing works
Expression of emotional–motivational connotations with a one-word utterance
Leinonen, L. M., Hiltunen, T., Linnankoski, I., & Laakso, M. (1997) · The Journal of the Acoustical Society of America · doi:10.1121/1.420109 · Cited by 96 · see citing works
Scherer, K. R. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-287 · Cited by 76 · see citing works
Recognizing emotions from student speech in tutoring dialogues
Litman, D. J., & Forbes-Riley, K. (2004) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2003.1318398 · Cited by 74 · see citing works
(2002) · doi:10.1109/icme.2002.1035887 · Cited by 69 · see citing works
Self-citationSpeaker dependent emotion recognition using speech signals
Kang, B. S., Han, C.-H., Lee, S.-T., Youn, D.-H., & Lee, C. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-288 · Cited by 41 · see citing works
Self-citation
Flagged first, then by citations
1 of the 19 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Survey on speech emotion recognition: Features, classification schemes, and databases
Ayadi, M. M. H. E., Kamel, M. S., & Karray, F. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.020 · Cited by 2,089 · see citing works
Analysis of emotion recognition using facial expressions, speech and multimodal information
Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004) · Proceedings of the 6th international conference on Multimodal interfaces · doi:10.1145/1027933.1027968 · Cited by 840 · see citing works
Self-citationSpeech emotion recognition using deep neural network and extreme learning machine
Han, K., Yu, D., & Tashev, I. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-57 · Cited by 824 · see citing works
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
Anagnostopoulos, C.-N., Iliou, T., & Giannoukos, I. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9368-5 · Cited by 428 · see citing works
Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies
Schuller, B. W., Vlasenko, B., Eyben, F., Wöllmer, M., Stuhlsatz, A., Wendemuth, A., & Rigoll, G. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.8 · Cited by 386 · see citing works
Databases, features and classifiers for speech emotion recognition: a review
Swain, M., Routray, A., & Kabisatpathy, P. (2018) · International Journal of Speech Technology · doi:10.1007/s10772-018-9491-z · Cited by 360 · see citing works
Ensemble methods for spoken emotion recognition in call-centres
Morrison, D., Wang, R., & De Silva, L. C. (2007) · Speech Communication · doi:10.1016/j.specom.2006.11.004 · Cited by 320 · see citing works
Class-level spectral features for emotion recognition
Bitouk, D., Verma, R., & Nenkova, A. (2010) · Speech Communication · doi:10.1016/j.specom.2010.02.010 · Cited by 219 · see citing works
Acoustic feature selection for automatic emotion recognition from speech
Rong, J., Li, G., & Chen, Y. P. (2008) · Information Processing & Management · doi:10.1016/j.ipm.2008.09.003 · Cited by 211 · see citing works
Social Touch in Human–Computer Interaction
van Erp, J. B. F., & Toet, A. (2015) · Frontiers in Digital Humanities · doi:10.3389/fdigh.2015.00002 · Cited by 202 · see citing works
GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition
Hu, H., Xu, M., & Wu, W. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366937 · Cited by 201 · see citing works
An experimental study of speech emotion recognition based on deep convolutional neural networks
Zheng, W. Q., Yu, J. S., & Zou, Y. X. (2015) · 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2015.7344669 · Cited by 187 · see citing works
Emotion Recognition From Speech With Recurrent Neural NetworksPreprint
Chernykh, V., & Prikhodko, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08071 · Cited by 140 · see citing works
Survey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137 · see citing works
Jointly Predicting Arousal, Valence and Dominance with Multi-Task Learning
Parthasarathy, S., & Busso, C. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1494 · Cited by 136 · see citing works
Self-citationVogt, T., André, E., & Wagner, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85099-1_7 · Cited by 129 · see citing works
A Mutual Information Based Adaptive Windowing of Informative EEG for Emotion Recognition
Piho, L., & Tjahjadi, T. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2840973 · Cited by 122 · see citing works
Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech
Cao, H., Verma, R., & Nenkova, A. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.01.003 · Cited by 119 · see citing works
Filippini, C., Perpetuini, D., Cardone, D., Chiarelli, A. M., & Merla, A. (2020) · Applied Sciences · doi:10.3390/app10082924 · Cited by 119 · see citing works
Robust Detection of COVID-19 in Cough Sounds
Mouawad, P. G., Dubnov, T., & Dubnov, S. (2021) · SN Computer Science · doi:10.1007/s42979-020-00422-6 · Cited by 109 · see citing works
Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges
Tahon, M., & Devillers, L. Y. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2487051 · Cited by 98 · see citing works
Meng, H., Bianchi‐Berthouze, N., Deng, Y. S., Cheng, J., & Cosmas, J. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2418092 · Cited by 94 · see citing works
End-to-End Speech Emotion Recognition With Gender Information
Sun, T.-W. (2020) · IEEE Access · doi:10.1109/access.2020.3017462 · Cited by 88 · see citing works
Emotion Detection from Speech Signals using Voting Mechanism on Classified Frames
Zamil, A. A. A., Hasan, S., Baki, S. M. J., Adam, J. M., & Zaman, I. (2019) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest.2019.8644168 · Cited by 87 · see citing works
Exploring Cross-Modality Affective Reactions for Audiovisual Emotion Recognition
Mariooryad, S., & Busso, C. (2013) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2013.11 · Cited by 82 · see citing works
Self-citationUsing neutral speech models for emotional speech analysis
Busso, C., Lee, S., & Narayanan, S. S. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-605 · Cited by 82 · see citing works
Self-citationAffective State Level Recognition in Naturalistic Facial and Vocal Expressions
Meng, H., & Bianchi‐Berthouze, N. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2253768 · Cited by 75 · see citing works
The Automatic Recognition of Emotions in Speech
Batliner, A. M., Schuller, B. W., Seppi, D., Steidl, S., Devillers, L. Y., Vidrascu, L., Vogt, T., Aharonson, V., & Amir, N. (2010) · Cognitive technologies · doi:10.1007/978-3-642-15184-2_6 · Cited by 75 · see citing works
Multimodal emotion recognition with evolutionary computation for human-robot interaction
Pérez-Gaspar, L.-A., Caballero‐Morales, S., & Trujillo-Romero, F. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.08.047 · Cited by 75 · see citing works
Analysis of Emotional Speech—A Review
Gangamohan, P., Kadiri, S. R., & Yegnanarayana, B. (2016) · Intelligent systems reference library · doi:10.1007/978-3-319-31056-5_11 · Cited by 72 · see citing works
Liu, Z., Rehman, A., Wu, M., Cao, W., & Hao, M. (2021) · Information Sciences · doi:10.1016/j.ins.2021.02.016 · Cited by 70 · see citing works
Emotion dimensions and formant position
Goudbeek, M. B., Goldman, J.-P., & Scherer, K. R. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-469 · Cited by 67 · see citing works
Formant position based weighted spectral features for emotion recognition
Bozkurt, E., Erzin, E., Erdem, Ç. E., & Erdem, A. T. (2011) · Speech Communication · doi:10.1016/j.specom.2011.04.003 · Cited by 64 · see citing works
Shape-based modeling of the fundamental frequency contour for emotion detection in speech
Arias, J. P., Busso, C., & Yoma, N. B. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.07.002 · Cited by 62 · see citing works
Self-citationEmotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions
Deb, S., & Dandapat, S. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2730187 · Cited by 56 · see citing works
Emotion recognition from speech signals via a probabilistic echo-state network
Trentin, E., Scherer, S. J., & Schwenker, F. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.015 · Cited by 60 · see citing works
Savran, A., Cao, H., Shah, M., Nenkova, A., & Verma, R. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388781 · Cited by 59 · see citing works
Comparison of Different Classifiers for Emotion Recognition
Iliou, T., & Anagnostopoulos, C. (2009) · Panhellenic Conference on Informatics · doi:10.1109/pci.2009.7 · Cited by 58 · see citing works
Choice of a classifier, based on properties of a dataset: case study-speech emotion recognition
Koolagudi, S. G., Murthy, Y. V. S., & Bhaskar, S. P. (2018) · International Journal of Speech Technology · doi:10.1007/s10772-018-9495-8 · Cited by 58 · see citing works
Meng, H., & Bianchi‐Berthouze, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_49 · Cited by 57 · see citing works
Exploring the Contextual Factors Affecting Multimodal Emotion Recognition in Videos
Bhattacharya, P., Gupta, R., & Yang, Y. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3071503 · Cited by 49 · see citing works
Chubb, J., Missaoui, S., Concannon, S. J., Maloney, L., & Walker, J. A. (2021) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2021.100403 · Cited by 56 · see citing works
Goulart, C., Valadão, C., Delisle-Rodríguez, D., Funayama, D., Favarato, A., Baldo, G., Binotte, V., Caldeira, E. M. D. O., & Bastos-Filho, T. (2019) · Sensors · doi:10.3390/s19132844 · Cited by 55 · see citing works
Acoustic Features Extraction for Emotion Recognition
Rong, J., Chen, Y. P., Chowdhury, M. U., & Li, G. (2007) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2007.48 · Cited by 54 · see citing works
Tan, C., Ceballos, G., Kasabov, N., & Subramaniyam, N. P. (2020) · Italian National Conference on Sensors · doi:10.3390/s20185328 · Cited by 43 · see citing works
Compensating for speaker or lexical variabilities in speech for emotion recognition
Mariooryad, S., & Busso, C. (2013) · Speech Communication · doi:10.1016/j.specom.2013.07.011 · Cited by 52 · see citing works
Self-citationPerceiving emotion: towards a realistic understanding of the task
Cowie, R. (2009) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0139 · Cited by 52 · see citing works
Statistical Evaluation of Speech Features for Emotion Recognition
Iliou, T., & Anagnostopoulos, C. (2009) · International Conference on Digital Telecommunications (ICDT) · doi:10.1109/icdt.2009.30 · Cited by 50 · see citing works
Kolmogorov-Smirnov test for feature selection in emotion recognition from speech
Ivanov, A., & Riccardi, G. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6289074 · Cited by 50 · see citing works
Byun, S.-W., & Lee, S.-P. (2021) · Applied Sciences · doi:10.3390/app11041890 · Cited by 50 · see citing works
Self-citationRobust emotion recognition in noisy speech via sparse representation
Zhao, X., Zhang, S., & Lei, B. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1377-z · Cited by 49 · see citing works
Multimodal Relational Tensor Network for Sentiment and Emotion Classification
Sahay, S., Kumar, S. H., Xia, R., Huang, J., & Nachman, L. (2018) · doi:10.18653/v1/w18-3303 · Cited by 46 · see citing works
eXplainable Cooperative Machine Learning with NOVA
Baur, T., Heimerl, A., Lingenfelser, F., Wagner, J., Valstar, M., Schuller, B. W., & André, E. (2020) · Künstliche Intell. · doi:10.1007/s13218-020-00632-3 · Cited by 46 · see citing works
Classifier Fusion for Emotion Recognition from Speech
Scherer, S. J., Schwenker, F., & Palm, G. (2009) · Springer eBooks · doi:10.1007/978-0-387-76485-6_5 · Cited by 45 · see citing works
Emotion recognition from speech VIA boosted Gaussian mixture models
Tang, H., Chu, S. M., Hasegawa‐Johnson, M., & Huang, T. S. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202493 · Cited by 45 · see citing works
Vlasenko, B., Prylipko, D., Böck, R., & Wendemuth, A. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.11.003 · Cited by 40 · see citing works
High-Performance Modelling and Simulation for Big Data Applications
Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37 · see citing works
Real-Time Emotion Recognition from Speech Using Echo State Networks
Scherer, S. J., Oubbati, M., Schwenker, F., & Palm, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69939-2_20 · Cited by 34 · see citing works
Vowels formants analysis allows straightforward detection of high arousal emotions
Vlasenko, B., Philippou-Hübner, D., Prylipko, D., Böck, R., Siegert, I., & Wendemuth, A. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6012003 · Cited by 34 · see citing works
Review on Speech Emotion Recognition
Wen, H. (2014) · Cited by 34 · see citing works
Alex, S. B., Mary, L., & Babu, B. P. (2020) · Circuits Systems and Signal Processing · doi:10.1007/s00034-020-01429-3 · Cited by 34 · see citing works
Emotion Classification from Speech and Text in Videos Using a Multimodal Approach
Caschera, M. C., Grifoni, P., & Ferri, F. (2022) · Multimodal Technologies and Interaction · doi:10.3390/mti6040028 · Cited by 34 · see citing works
Speech Based Emotion Recognition
Sethu, V., Epps, J., & Eliathamby Ambikairajah, A. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1456-2_7 · Cited by 32 · see citing works
Formant Frequencies under Cognitive Load: Effects and Classification
Yap, T. F., Epps, J., Eliathamby Ambikairajah, A., & Choi, E. H. C. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2011/219253 · Cited by 32 · see citing works
Investigating the role of phoneme-level modifications in emotional speech resynthesis
Bulut, M., Busso, C., Yıldırım, S., Kazemzadeh, A., Lee, C. M., Lee, S., & Narayanan, S. S. (2005) · Interspeech (USB) · doi:10.21437/interspeech.2005-378 · Cited by 32 · see citing works
Self-citationEmotions Recognition By Speech And Facial Expressions Analysis
Apatean, A., Emerich, S., & Lupu, E. (2009) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.41698 · Cited by 18 · see citing works
Dimensionality reduction-based spoken emotion recognition
Zhang, S., & Zhao, X. (2011) · Multimedia Tools and Applications · doi:10.1007/s11042-011-0887-x · Cited by 29 · see citing works
Lalitha, S., Gupta, D., Zakariah, M., & Alotaibi, Y. A. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107519 · Cited by 29 · see citing works
Towards Emotion Recognition in Human Computer Interaction
Palm, G., & Glodek, M. (2012) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-35467-0_32 · Cited by 28 · see citing works
Automatic speech based emotion recognition using paralinguistics features
Hook, J., Noroozi, F., Toygar, Ö., & Anbarjafari, G. (2019) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpasts.2019.129647 · Cited by 28 · see citing works
An Ensemble Framework of Voice-Based Emotion Recognition System for Films and TV Programs
Tao, F., Liu, G., & Zhao, Q. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461617 · Cited by 27 · see citing works
Using Agreement on Direction of Change to Build Rank-Based Emotion Classifiers
Parthasarathy, S., Cowie, R., & Busso, C. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2593944 · Cited by 27 · see citing works
Self-citationA vowel based approach for acted emotion recognition
Ringeval, F., & Chétouani, M. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-685 · Cited by 27 · see citing works
(2016) · doi:10.1145/2988257.2988265 · Cited by 26 · see citing works
Kim, Y., & Provost, E. M. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654934 · Cited by 24 · see citing works
Kallipolitis, A., Galliakis, M., Menychtas, A., & Maglogiannis, I. G. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05203-z · Cited by 24 · see citing works
Ranking emotional attributes with deep neural networks
Parthasarathy, S., Lotfian, R., & Busso, C. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953107 · Cited by 24 · see citing works
Self-citationAnger Detection in Arabic Speech Dialogs
Khalil, A., Al-Khatib, W. G., El-Alfy, E.-S. M., & Cheded, L. (2018) · International Conference on Computing Sciences and Engineering (ICCSE) · doi:10.1109/iccse1.2018.8374203 · Cited by 24 · see citing works
Fusion of global statistical and segmental spectral features for speech emotion recognition
Hu, H., Xu, M., & Wu, W. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-616 · Cited by 24 · see citing works
Caballero‐Morales, S. (2013) · The Scientific World JOURNAL · doi:10.1155/2013/162093 · Cited by 24 · see citing works
Comparative Deep Network Analysis of Speech Emotion Recognition Models using Data Augmentation
Prasanna, Y. L., Tarakaram, Y., Mounika, Y., Palaniswamy, S., & Vekkot, S. (2022) · International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) · doi:10.1109/centcon56610.2022.10051557 · Cited by 23 · see citing works
The GMM-SVM Supervector Approach for the Recognition of the Emotional Status from Speech
Schwenker, F., Scherer, S. J., Magdi, Y. M., & Palm, G. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04274-4_92 · Cited by 23 · see citing works
Emotion Recognition During Speech Using Dynamics of Multiple Regions of the Face
Kim, Y., & Provost, E. (2015) · ACM Trans. Multim. Comput. Commun. Appl. · doi:10.1145/2808204 · Cited by 26 · see citing works
A rule-based emotion-dependent feature extraction method for emotion analysis from speech
Hozjan, V., & Kačič, Z. (2006) · The Journal of the Acoustical Society of America · doi:10.1121/1.2188647 · Cited by 23 · see citing works
Guthier, B., Dörner, R., & Martínez, H. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46152-6_16 · Cited by 22 · see citing works
SVM-MLP-PNN Classifiers on Speech Emotion Recognition Field - A Comparative Study
Iliou, T., & Anagnostopoulos, C. (2010) · International Conference on Digital Telecommunications (ICDT) · doi:10.1109/icdt.2010.8 · Cited by 21 · see citing works
Huang, Z., & Epps, J. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2821135 · Cited by 21 · see citing works
Exploiting Acoustic and Lexical Properties of Phonemes to Recognize Valence from Speech
Zhang, B., Khorram, S., & Provost, E. M. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683190 · Cited by 21 · see citing works
A Multilevel Fusion Approach for Audiovisual Emotion Recognition
Chetty, G., Wagner, M., & Goecke, R. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch17 · Cited by 21 · see citing works
Lee, C.-C., Chaspari, T., Provost, E. M., & Narayanan, S. S. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3276209 · Cited by 20 · see citing works
Self-citationLexical Dependent Emotion Detection Using Synthetic Speech Reference
Lotfian, R., & Busso, C. (2019) · IEEE Access · doi:10.1109/access.2019.2898353 · Cited by 20 · see citing works
Self-citationEmotion Recognition from Speech Using Multi-Classifier Systems and RBF-Ensembles
Scherer, S. J., Schwenker, F., & Palm, G. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-75398-8_3 · Cited by 19 · see citing works
Kächele, M., Zharkov, D., Meudt, S., & Schwenker, F. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.148 · Cited by 19 · see citing works
Keystroke dynamics in the pre-touchscreen era
Ahmad, N., Szymkowiak, A., & Campbell, P. A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00835 · Cited by 19 · see citing works
Preference-Learning with Qualitative Agreement for Sentence Level Emotional Annotations
Parthasarathy, S., & Busso, C. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2478 · Cited by 19 · see citing works
Self-citationSurvey on Human Emotion Recognition: Speech Database, Features and Classification
Singh, Y. B., & Goel, S. (2018) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn.2018.8748379 · Cited by 19 · see citing works
Joint Analysis of the Emotional Fingerprint in the Face and Speech: A single subject study
Busso, C., & Narayanan, S. S. (2007) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2007.4412814 · Cited by 19 · see citing works
Self-citationTowards Emotion Recognition from Speech: Definition, Problems and the Materials of Research
Anagnostopoulos, C., & Iliou, T. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-11684-1_8 · Cited by 18 · see citing works
Meudt, S., Zharkov, D., Kächele, M., & Schwenker, F. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2531743 · Cited by 18 · see citing works
- Authors
- Lee, C. M., Yildirim, S., Bulut, M., Kazemzadeh, A., Busso, C., Deng, Z., Lee, S., & Narayanan, S.
- Affiliations
- University of Southern California (US)
- Venue
- Interspeech 2004
- Publisher
- ISCA
- Published
- 2004-10-04
- Pages
- 889-892
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W1668904664
- Cited by
- 228
- References
- 19
- Citations by year
- 2025: 5 · 2024: 5 · 2023: 9 · 2022: 10 · 2021: 12 · 2020: 17 · 2019: 11 · 2018: 21 · 2017: 12 · 2016: 10 · 2015: 15 · 2014: 12 · 2013: 13 · 2012: 7
- FWCI
- 17.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- emotion recognition, speech emotion recognition, phonetic classes, hidden Markov models, emotional speech, prosodic features, decision fusion, anger, happiness, sadness, neutral emotion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
