Carlos Busso

Ph.D - Electrical Engineering
University of Southern California - 2008

M.S. - Electrical Engineering
University of Chile - 2003

ENG. - Electrical Engineering
University of Chile - 2003

B.S. - Electrical Engineering
University of Chile - 2000

Soroosh Mariooryad and Carlos Busso, "Compensating for speaker or lexical variabilities in speech for emotion recognition," Speech Communication, vol. 57, pp. 1-12, February 2014. 2014 - Publication

Nanxiang Li, Jinesh J. Jain, and Carlos Busso, "Modeling of driver behavior in real world scenarios using multiple noninvasive sensors," IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1213-1225, August 2013. 2013 - Publication

Carlos Busso, Murtaza Bulut, and Shrikanth S. Narayanan, "Toward effective automatic recognition systems of emotion in speech," in Social emotions in nature and artifact: emotions in human and human-computer interaction, S. Marsella J. Gratch, Ed. Oxford University Press, New York, NY, USA, 2013. 2013 - Publication

Soroosh Mariooryad and Carlos Busso, "Exploring cross-modality affective reactions for audiovisual emotion recognition," IEEE Transactions on Affective Computing, vol. 4, no. 2, pp. 183-196, April-June 2013. 2013 - Publication

Carlos Busso, Soroosh Mariooryad, Angeliki Metallinou, and Shrikanth S. Narayanan, " Iterative feature normalization scheme for automatic emotion detection from speech," IEEE Transactions on Affective Computing, vol. In press, 2013. 2013 - Publication

Juan Pablo Arias, Carlos Busso, and Nestor Becerra Yoma, "Shape-based modeling of the fundamental frequency contour for emotion detection in speech," Computer Speech and Language, vol. In Press, 2013. 2013 - Publication

Soroosh Mariooryad and Carlos Busso, "Generating human-like behaviors using joint, speech-driven models for conversational agents," IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no. 8, pp. 2329-2340, October 2012. 2012 - Publication

Carlos Busso and Jinesh J. Jain, "Advances in multimodal tracking of driver distraction," in DSP for In-Vehicle Systems & Safety, J. Hansen, P. Boyraz, K. Takeda, and H. Abut, Eds., p. In Press. Springer, New York, NY, USA, 2012. 2012 - Publication

Professor Is Designing Tools to Help Computers Sense Emotion

Dr. Carlos Busso hopes computers will one day sense how you’re feeling. The associate professor of electrical engineering is designing speech recognition tools that understand human emotion.  
To further his research, Busso has received a National Science Foundation Faculty Early Career Development (CAREER) Award, which provides nearly $500,000 in funding over the next five years.

Engineering Professor Earns Award for Influential Audiovisual Study

Dr. Carlos Busso, assistant professor of electrical engineering in the Erik Jonsson School of Engineering and Computer Science, is the inaugural recipient of a 10-Year Technical Impact Award given by the Association for Computing Machinery International Conference on Multimodal Interaction.

The award was given for Busso’s work on one of the first studies about audiovisual emotion recognition. The work analyzed the limitations in solely detecting emotions from speech or facial recognition, and discussed the benefits of using both modalities at the same time.

Carlos Busso Lands $1 Million Grant from NSF

Dr. Carlos Busso received $1,075,386 from the National Science Foundation for his research on Creating The Largest Speech Emotional Database By Leveraging Existing Naturalistic Recordings. The project focuses on developing the MSP-Podcast corpus, which would be the largest, publicly available, naturalistic speech emotional database. The MSP-Podcast will provide infrastructure to address automatic speech recognition and speaker verification solutions against variations due to emotional content.

Carlos Busso Lands $1 Million Grant from NSF

Dr. Carlos Busso received $1,075,386 from the National Science Foundation for his research on Creating The Largest Speech Emotional Database By Leveraging Existing Naturalistic Recordings. The project focuses on developing the MSP-Podcast corpus, which would be the largest, publicly available, naturalistic speech emotional database. The MSP-Podcast will provide infrastructure to address automatic speech recognition and speaker verification solutions against variations due to emotional content.