The INTERSPEECH 2009 emotion challenge

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797

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

Speech recognition by machines and humans

Lippmann, R. P. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00021-6 · Cited by 531

The Vera am Mittag German audio-visual emotional speech database

Grimm, M., Kroschel, K., & Narayanan, S. S. (2008) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2008.4607572 · Cited by 413

The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals

Schuller, B. W., Batliner, A. M., Seppi, D., Steidl, S., Vogt, T., Wagner, J., Devillers, L. Y., Vidrascu, L., Amir, N., Kessous, L., et al. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-612 · Cited by 191

Combining Efforts for Improving Automatic Classification of Emotional User States

Batliner, A., Steidl, S., Schuller, B., Seppi, D., Laskowski, K., Vogt, T., Devillers, L., Vidrascu, L., Amir, N., Kessous, L., et al. (2006) · Jožef Stefan Institute, Ljubljana · doi:10.5281/zenodo.14167504 · Cited by 119

A State of the Art Review on Emotional Speech Databases

Ververidis, D., & Kotropoulos, K. (2003) · Cited by 88

Audiovisual recognition of spontaneous interest within conversations

Schuller, B. W., Müeller, R., Hörnler, B., Hoethker, A., Konosu, H., & Rigoll, G. (2007) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1322192.1322201 · Cited by 85

Patterns, prototypes, performance: classifying emotional user states

Seppi, D., Batliner, A. M., Schuller, B. W., Steidl, S., Vogt, T., Wagner, J., Devillers, L. Y., Vidrascu, L., Amir, N., & Aharonson, V. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-193 · Cited by 41

Comparing data-Driven and Handcrafted Features for Dimensional Emotion Recognition

Vlasenko, B., Vyas, S., & Magimai.-Doss, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446134 · Cited by 5

Automatic Speech-Based Classification of Gender, Age and Accent

Nguyen, P., Tran, D., Huang, X., & Sharma, D. K. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15037-1_24 · Cited by 5

Depression Detection & Emotion Classification via Data-Driven Glottal Waveforms

Vandyke, D. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.112 · Cited by 5

Exploratory analysis of speech features related to depression in adults with Aphasia

Gillespie, S., Moore, E., Laures-Gore, J. S., & Farina, M. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472792 · Cited by 5

Automatic formatted transcripts for videos

Pappu, A., & Stent, A. J. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-542 · Cited by 5

Agreement-based Dynamic Active Learning with Least and Medium Certainty Query Strategy

Zhang, Y., Coutinho, E., Zhang, Z., Quan, C. Z., & Schuller, B. W. (2015) · OPUS (Augsburg University) · Cited by 5

Two-stream Emotion-embedded Autoencoder for Speech Emotion Recognition

Zhang, C., & Xue, L. (2021) · IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · doi:10.1109/iemtronics52119.2021.9422602 · Cited by 5

Audio-Visual Emotion Recognition System Using Multi-Modal Features

Handa, A., Agarwal, R., & Kohli, N. (2021) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.20211001.oa34 · Cited by 5

Initial Analysis of the Impact of Emotional Speech on the Performance of Speaker Recognition on New Serbian Emotional Database

Mandaric, I., Vujović, M., Suzić, S., Nosek, T., Simić, N., & Delić, V. (2021) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor52709.2021.9653376 · Cited by 5

Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method

Lin, R. F., Leung, T., Liu, Y.-P., & Hu, K.-R. (2022) · Healthcare · doi:10.3390/healthcare10050935 · Cited by 5

Emotion Recognition System for Arabic Speech: Case Study Egyptian Accent

Seknedy, M. E., & Fawzi, S. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21595-7_8 · Cited by 5

Using Hybrid HMM/DNN Embedding Extractor Models in Computational Paralinguistic Tasks

Vetráb, M., & Gosztolya, G. (2023) · Sensors · doi:10.3390/s23115208 · Cited by 5

Speaker dependent emotion recognition using prosodic supervectors

Moreno, I. L., Ortego-Resa, C., González-Rodríguez, J., & Ramos, D. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-477 · Cited by 5

Emotion Recognition in Real-world Conditions with Acoustic and Visual Features

Yuryevich, S. M., & Minker, W. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666279 · Cited by 5

Emotion recognition method based on normalization of prosodic features

Suzuki, M., Nakagawa, S., & Kita, K. (2013) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2013.6694147 · Cited by 5

The SYSU System for CCPR 2016 Multimodal Emotion Recognition Challenge

He, G., Chen, J., Liu, X., & Li, M. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_58 · Cited by 5

Affective Interaction with a Companion Robot for Hospitalized Children: a Linguistically based Model for Emotion Detection

Tallec, M. L., Antoine, J.-Y., Villaneau, J., & Duhaut, D. (2011) · HAL · Cited by 5

A Deep-Learning-Based Method of Estimating Water Intake

Yamada, Y., Saito, T., Kawasaki, S., Ikeda, D., Katagiri, M., Nishimura, M., & Mineno, H. (2017) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2017.14 · Cited by 5

Discriminating Neutral and Emotional Speech using Neural Networks

Kadiri, S. R., Gangamohan, P., & Yegnanarayana, B. (2014) · International conference natural language processing · Cited by 5

Detection of challenging dialogue stages using acoustic signals and biosignals

Egorow, O., & Wendemuth, A. (2016) · Digital Library (University of West Bohemia) · Cited by 5

Construction of Spontaneous Emotion Corpus from Indonesian TV Talk Shows and Its Application on Multimodal Emotion Recognition

Lubis, N., Lestari, D. P., Sakti, S., Purwarianti, A., & Nakamura, S. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017edp7362 · Cited by 5

RUC at MediaEval 2016: Predicting Media Interestingness Task.

Chen, S., Dian, Y., & Jin, Q. (2016) · MediaEval · Cited by 5

Multi-condition training for noise-robust speech emotion recognition

Chiba, Y., Nose, T., & Ito, A. (2019) · Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi · doi:10.1250/ast.40.406 · Cited by 5

Recognizing Social Signals with Weakly Supervised Multitask Learning for Multimodal Dialogue Systems

Hirano, Y., Okada, S., & Komatani, K. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479927 · Cited by 5

Comparison of Static and Time-Sequential Features in Automatic Fluency Detection of Spontaneous Speech

Deng, H., Utsuro, T., Kobayashi, A., & Nishizaki, H. (2021) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda202152914.2021.9660601 · Cited by 5

Grouped Echo State Network with Late Fusion for Speech Emotion Recognition

Ibrahim, H., Loo, C. K., & Alnajjar, F. S. K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92238-2_36 · Cited by 5

Implicitly Aligning Joint Distributions for Cross-Corpus Speech Emotion Recognition

Lu, C., Zong, Y., Tang, C., Lian, H., Chang, H., Zhu, J., Li, S., & Zhao, Y. D. (2022) · Electronics · doi:10.3390/electronics11172745 · Cited by 5

Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities

Mirheidari, B., Bittar, A., Cummins, N., Downs, J. M., Fisher, H. L., & Christensen, H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10188 · Cited by 5

Dialog Breakdown Detection using Multimodal Features for Non-task-oriented Dialog Systems

Tsubokura, K., Iribe, Y., & Kitaoka, N. (2022) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce56475.2022.10014052 · Cited by 5

A Study of Correlation between Physiological Process of Articulation and Emotions on Mandarin Chinese

Zhang, Z., Huang, M., & Xiao, Z. (2022) · Speech Communication · doi:10.1016/j.specom.2023.01.005 · Cited by 5

Automatic minute generation for parliamentary speech using conditional random fields

Zhang, J. J., Fung, P., & Chan, R. H. Y. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947613 · Cited by 5

A study of social-affective communication: Automatic prediction of emotion triggers and responses in television talk shows

Lubis, N., Sakti, S., Neubig, G., Yoshino, K., Toda, T., & Nakamura, S. (2015) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2015.7404867 · Cited by 5

A longitudinal database of Irish political speech with annotations of speaker ability

Cullen, A., & Harte, N. (2017) · Language Resources and Evaluation · doi:10.1007/s10579-017-9401-z · Cited by 5

Dramatic action: A theater-based paradigm for analyzing human interactions

Liron, Y., Raindel, N., & Alon, U. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193404 · Cited by 6

A noise classification algorithm based on SAMME and BP neural network

Guoqiang, C., Yuying, Z., & Deyou, T. (2018) · IEEE International Conference on Big Data Analysis (ICBDA) · doi:10.1109/icbda.2018.8367691 · Cited by 5

Learning to Interrupt the User at the Right Time in Incremental Dialogue Systems

Chýlek, A., Švec, J., & Šmídl, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00794-2_54 · Cited by 5

Contemporary Methods for Speech Parameterization

Ganchev, T. D. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8447-0_1 · Cited by 5

On the relevance of high-level features for speaker independent emotion recognition of spontaneous speech

Lugger, M., & Yang, B. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-483 · Cited by 5

Level of interest sensing in spoken dialog using decision-level fusion of acoustic and lexical evidence

Jeon, J. H., Xia, R., & Liu, Y. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.09.005 · Cited by 5

Perception and prediction of speaker appeal – A single speaker study

Cullen, A., Hines, A., & Harte, N. (2018) · Computer Speech & Language · doi:10.1016/j.csl.2018.04.004 · Cited by 5

Learnable Graph Inception Network for Emotion Recognition.Preprint

Shirian, A., Tripathi, S., & Guha, T. (2020) · arXiv (Cornell University) · Cited by 5

Unsupervised speech representation learning for behavior modeling using triplet enhanced contextualized networks

Li, H., Baucom, B., Narayanan, S. S., & Georgiou, P. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101226 · Cited by 4

Real-time Incremental Processing

Eyben, F. (2015) · Springer theses · doi:10.1007/978-3-319-27299-3_4 · Cited by 4

Public-Private Attributes-Based Variational Adversarial Network for Audio-Visual Cross-Modal Matching

Zheng, A., Yuan, F., Zhang, H., Wang, J., Tang, C., & Li, C. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3390573 · Cited by 4

Speech-based Clinical Depression Detection: An Empirical Study

Chen, Y., Xu, C., Liang, C., Tao, Y., & Shi, C. (2025) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw65056.2025.11011046 · Cited by 4

Acoustic feature selection utilizing multiple kernel learning for classification of children with autism spectrum and typically developing children

Kakihara, Y., Takiguchi, T., Ariki, Y., Nakai, Y., & Takada, S. (2013) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2013.6776604 · Cited by 4

University of Ljubljana system for interspeech 2011 speaker state challenge

Gajšek, R., Dobrišek, S., & Mihelič, F. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-812 · Cited by 4

Emotion classification of infant cries with consideration for local and global features

Honda, K., Kitahara, K., Matsunaga, S., Yamashita, M., & Shinohara, K. (2012) · Cited by 4

Towards a corpus of speech emotion for interactive dialog systems

Bertero, D., Siddique, F. B., & Fung, P. (2016) · International Conference on Speech Database and Assessments · doi:10.1109/icsda.2016.7919019 · Cited by 4

Evaluation of quantitative and qualitative features for the acoustic analysis of domestic dogs’ vocalizations

Pérez-Espinosa, H., & Torres-García, A. A. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179050 · Cited by 4

Large-scale Data Collection and Analysis via a Gamified Intelligent Crowdsourcing Platform

Hantke, S., Olenyi, T., Hausner, C., Appel, T., & Schuller, B. W. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1180-0 · Cited by 4

Language Transfer of Audio Word2Vec: Learning Audio Segment Representations Without Target Language Data

Shen, C.-H., Sung, J. Y., & Lee, H.-Y. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461305 · Cited by 4

Emotion recognition from speech signal using fuzzy clustering

Stefano Rovetta, A., Mnasri, Z., Masulli, F., & Cabri, A. (2019) · European Society for Fuzzy Logic and Technology Conference · doi:10.2991/eusflat-19.2019.19 · Cited by 4

An Investigation on the Effectiveness of Multimodal Fusion and Temporal Feature Extraction in Reactive and Spontaneous Behavior Generative RNN Models for Listener Agents

Huang, H., Fukuda, M., & Nishida, T. (2019) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3349537.3351908 · Cited by 4

Effective Speech Features for Distinguishing Mild Dementia Patients from Healthy Person

Nishikawa, K., Hirakawa, R., Kawano, H., Nakashi, K., & Nakatoh, Y. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55307-4_54 · Cited by 4

A Two-Stage Attention Based Modality Fusion Framework for Multi-Modal Speech Emotion Recognition

Dongni, H. U., Chen, C., Zhang, P., Li, J., Yan, Y., & Zhao, Q. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edl8002 · Cited by 4

The JinYue Database for Huqin Music Emotion, Scene and Imagery Recognition

Zhang, K., Wu, X., Tang, R., Huang, Q., Yang, C., & Zhang, H. (2021) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr51284.2021.00059 · Cited by 4

A Novel Speech Emotion Model Based on CNN and LSTM Networks

Ye, B., Yuan, X., Peng, G., & Zeng, W. (2022) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait56212.2022.10137926 · Cited by 4

Findings of WASSA 2023 Shared Task on Empathy, Emotion and Personality Detection in Conversation and Reactions to News Articles

Barrière, V., Sedoc, J., Tafreshi, S., & Giorgi, S. (2023) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2023.wassa-1.44 · Cited by 5

Speech Emotion Recognition Based on Hyper-Prosodic Features

Jin, B., & Liu, G. (2017) · International Conference on Computer Technology, Electronics and Communication (ICCTEC) · doi:10.1109/icctec.2017.00027 · Cited by 4

Prediction of Inter-Personal Trust and Team Familiarity From Speech: A Double Transfer Learning Approach

Sandoval, C., Panganiban, A. R., Stolar, M. N., Bolia, R. S., & Lech, M. (2020) · IEEE Access · doi:10.1109/access.2020.3044285 · Cited by 4

Emotion Recognition with a Kernel Quantum Classifier

Tamburini, F. (2014) · Pisa University Press eBooks · doi:10.12871/clicit2014222 · Cited by 4

Learning Paralinguistic Features from Audiobooks through Style Voice Conversion

Aldeneh, Z., Perez, M. C., & Provost, E. M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.377 · Cited by 4

A two-channel speech emotion recognition model based on raw stacked waveform

Zheng, C., Wang, C., & Jia, N. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12378-1 · Cited by 4

Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data

Duret, J., Estève, Y., & Parcollet, T. (2023) · ISCA Speech Synthesis Workshop (SSW) · doi:10.21437/ssw.2023-29 · Cited by 4

Emotion and Disposition Detection in Medical Machines: Chances and Challenges

Hartmann, K., Siegert, I., & Prylipko, D. (2014) · International series on intelligent systems, control and automation: science and engineering · doi:10.1007/978-3-319-08108-3_19 · Cited by 4

RANSAC-Based Training Data Selection on Spectral Features for Emotion Recognition from Spontaneous Speech

Bozkurt, E., Erzin, E., Erdem, Ç. E., & Erdem, A. T. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25775-9_3 · Cited by 4

Building a Recognition System of Speech Emotion and Emotional States

Feng, X., & Watada, J. (2013) · International Conference on Robot, Vision and Signal Processing · doi:10.1109/rvsp.2013.64 · Cited by 4

Automatic Labeling Affective Scenes in Spoken Conversations

Alam, F., Danieli, M., & Riccardi, G. (2018) · Topics in intelligent engineering and informatics · doi:10.1007/978-3-319-95996-2_6 · Cited by 4

Two-Layer Fuzzy Multiple Random Forest for Speech Emotion Recognition

Chen, L., Wu, M., Pedrycz, W., & Hirota, K. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-61577-2_6 · Cited by 4

Recognition of Human Emotion in Speech Using Modulation Spectral Features and Support Vector Machines

Wu, S. (2009) · QSpace (Queen's University Library) · Cited by 4

Unsupervised learning for expressive speech synthesis

Jauk, I. (2017) · doi:10.5821/dissertation-2117-113289 · Cited by 4

CoRePooL—Corpus for Resource‐Poor Languages

Ganesh, H. B. B., Lal, G. J., Jairam, R., Soman, K. P., Kamal, N. S., & Sharmila, B. (2024) · Wiley eBooks · doi:10.1002/9781394214624.ch10 · Cited by 4

FinnAffect: An affective speech corpus for spontaneous Finnish

Lahtinen, K., Mustanoja, L., & Räsänen, O. J. (2025) · Speech Communication · doi:10.1016/j.specom.2025.103327 · Cited by 3

Multimodal deep learning for cardiovascular disease detection using pulse wave and vocal signals: a prediction model development and validation study

Lyu, Y., Wu, H.-M., Chen, R., Hong, J., Chen, C.-F., Shi, Y., Yi-qin, W. A. N. G., Yan, H., & Xu, J. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03518-w · Cited by 3

Speaker Intimacy Estimation in Chat-Talks Based on Verbal and Non-Verbal Information

Chiba, Y., & Ito, A. (2024) · IEEE Access · doi:10.1109/access.2024.3507945 · Cited by 3

Low-rank joint distribution adaptation for cross-corpus speech emotion recognition

Li, S., Lu, C., Zhao, Y. D., Lian, H., Qi, T., & Zong, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113260 · Cited by 5

What Timing and Behavior Patterns Determine Speed Dating Success in Japan?

Azuma, N., Shikama, D., Ogushi, A., Onishi, T., Ishii, R., & Miyata, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720028 · Cited by 3

Improving Spontaneous Children’s Emotion Recognition by Acoustic Feature Selection and Feature-Level Fusion of Acoustic and Linguistic Parameters

Planet, S., & Iriondo, I. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25020-0_12 · Cited by 3

Evaluation of emotion recognition from speech

Bozkurt, E., & Erzin, E. (2012) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2012.6204809 · Cited by 3

Obtaining speech assets for judgement analysis on low-pass filtered emotional speech

Snel, J., & Cullen, C. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771358 · Cited by 3

A novel emotion recognition technique from voiced-speech

Uribe, A., Gómez, A., Olivares, M. B., Quintero, O. L., & Campo, D. (2017) · IEEE Colombian Conference on Automatic Control (CCAC) · doi:10.1109/ccac.2017.8276451 · Cited by 3

Personality Identification Using Auditory Nerve Modelling of Human Speech

Gokul, K., & Lalitha, S. (2018) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2018.8554815 · Cited by 3

Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs

Feng, K., Zanwar, P., Behzadan, A. H., & Chaspari, T. (2020) · doi:10.1145/3423268.3423584 · Cited by 3

Entheos: A Multimodal Dataset for Studying Enthusiasm

Viegas, C., & Alikhani, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.180 · Cited by 3

Early Childhood Anxiety and Depression Detection Based on Speech Using Machine Learning Analysis

Nandyal, S., & Swathi, P. S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0739-4_43 · Cited by 3

Speech Emotion Recognition Systems: A Cross-Language, Inter-racial, and Cross-Gender Comparison

Datta, D., Jiang, W., Vogel, C. M., & Ahmad, K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-28076-4_28 · Cited by 3

Automatic Audio Augmentation for Requests Sub-Challenge

Sun, Y., Xu, K., Liu, C., Dou, Y., & Qian, K. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612849 · Cited by 3

Examining the Impacts of Dialogue Content and System Automation on Affect Models in a Spoken Tutorial Dialogue System

Drummond, J., & Litman, D. J. (2011) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 3

Investigation of glottal features and annotation procedures for speech emotion recognition

Masaaki, T., Yamamoto, K., & Nakagawa, S. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820791 · Cited by 3

Emotional Triggers and Responses in Spontaneous Affective Interaction: Recognition, Prediction, and Analysis

Lubis, N., Sakti, S., Yoshino, K., & Nakamura, S. (2018) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.dsh-d · Cited by 3

Automatic Personality Perception from Speech in Mandarin

Zhu, M., Xie, X., Zhang, L., & Wang, J. (2018) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2018.8706692 · Cited by 4

Detecting Highlighted Video Clips Through Emotion-Enhanced Audio-Visual Cues

Hu, L., He, W., Zhang, L., Xu, T., Xiong, H., & Chen, E. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428252 · Cited by 3

Conversational Speech Emotion Recognition From Indonesian Spoken Language Using Recurrent Neural Network-Based Model

Adma, A. N. I., & Lestari, D. P. (2021) · International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) · doi:10.1109/icaicta53211.2021.9640273 · Cited by 3

Significance of Accurate Vowel Region Detection for Speech based Emotion Recognition

Thirumuru, R., & Krishna, G. (2021) · International Conference on Computing, Communication and Automation · doi:10.1109/iccca52192.2021.9666406 · Cited by 3

Investigation of noise-reverberation-robustness of modulation spectral features for speech-emotion recognition

Guo, T., Li, S., Unoki, M., & Okada, S. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980032 · Cited by 3

Multi-Level Attention-Based Categorical Emotion Recognition Using Modulation-Filtered Cochleagram

Peng, Z., Wen-hua, H., Li, Y., Du, Y., & Dang, J. (2023) · Applied Sciences · doi:10.3390/app13116749 · Cited by 3

Affect Recognition in Real Life Scenarios

Κωστούλας, Θ., Ganchev, T. D., & Fakotakis, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18184-9_37 · Cited by 3

Speech Summarization without Lexical Features for Mandarin Presentation Speech

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