Narayanan, Shrikanth

IEMOCAP: Interactive emotional dyadic motion capture database

Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. S. (2008) · Language Resources and Evaluation

Emotion and Mood RecognitionHuman Motion and Animation

Cited by 3,869

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., Devillers, L. Y., Epps, J., Laukka, P., Narayanan, S. S., et al. (2016) · IEEE Transactions on Affective Computing

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 1,818

Toward detecting emotions in spoken dialogs

Lee, C. M., & Narayanan, S. S. (2005) · IEEE Transactions on Speech and Audio Processing

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 988

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

Emotion and Mood RecognitionFace and Expression Recognition

Cited by 840

A Framework for Automatic Human Emotion Classification Using Emotion Profiles

Mower, E., Mataric, M. J., & Narayanan, S. (2011) · IEEE Transactions on Audio, Speech, and Language Processing

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 215

Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling

Wöllmer, M., Metallinou, A., Eyben, F., Schuller, B., & Narayanan, S. S. (2010) · Interspeech 2010

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 201

Machine learning and natural language processing in psychotherapy research: alliance as example use case.

Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., et al. (2020) · Journal of Counseling Psychology

Psychotherapy Techniques and ApplicationsMental Health via Writing

Cited by 141

Designing Contestability: Interaction Design, Machine Learning, and Mental Health

Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C. (2017) · Proceedings of the 2017 Conference on Designing Interactive Systems

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 110

Decision level combination of multiple modalities for recognition and analysis of emotional expression

Metallinou, A., Lee, S., & Narayanan, S. (2010) · 2010 IEEE International Conference on Acoustics, Speech and Signal Processing

Emotion and Mood RecognitionFace and Expression Recognition

Cited by 89

Analysis of user behavior under error conditions in spoken dialogs

Shin, J., Narayanan, S. S., Gerber, L., Kazemzadeh, A., & Byrd, D. (2002) · 7th International Conference on Spoken Language Processing (ICSLP 2002)

Speech and dialogue systemsSpeech Recognition and Synthesis

Cited by 54

Pathological speech processing: state-of-the-art, current challenges, and future directions

Gupta, R., Chaspari, T., Kim, J., Kumar, N., Bone, D., & Narayanan, S. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Voice and Speech DisordersSpeech Recognition and Synthesis

Cited by 40

PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Models

Feng, T., & Narayanan, S. (2023) · 2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 34

A Multi-task Approach to Learning Multilingual Representations

Singla, K., Can, D., & Narayanan, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 22

Redundancy analysis of behavioral coding for couples therapy and improved estimation of behavior from noisy annotations

Nasir, M., Baucom, B., Georgiou, P., & Narayanan, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Emotion and Mood RecognitionReliability and Agreement in Measurement

Cited by 9

Developing personalized algorithms for sensing mental health symptoms in daily life

Timmons, A. C., Tutul, A. A., Avramidis, K., Duong, J. B., Carta, K. E., Walters, S. N., Jumonville, G. A., Carrasco, A. S., Freitag, G. F., Romero, D. N., et al. (2025) · npj Mental Health Research

Digital Mental Health InterventionsMental Health Research Topics

Cited by 7

Scaling Wearable Foundation Models

Narayanswamy, G., Liu, X., Ayush, K., Yang, Y., Xu, X., Liao, S., Garrison, J., Tailor, S., Sunshine, J., Liu, Y., et al. (2024) · arXiv

Context-Aware Activity Recognition SystemsTime Series Analysis and Forecasting

Cited by 6

The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition

Chochlakis, G., Potamianos, A., Lerman, K., & Narayanan, S. (2024) · arXiv

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 5

Study of acoustic correlates associate with emotional speech

Yildirim, S., Lee, S., Lee, C. M., Bulut, M., Busso, C., Kazemzadeh, E., & Narayanan, S. (2004) · The Journal of the Acoustical Society of America

Emotion and Mood RecognitionPhonetics and Phonology Research

Cited by 0