Recognition of Spontaneous Emotions by Speech within Automotive Environment

Resumen

Within the car, recognition of emotion largely helps to design communication more natural. Speech interaction is here used more broadly today, and affective cues are contained within acoustic and linguistic parameters. However, we introduce novel concepts and results considering the estimation of a driver’s emotion by focusing on acoustic information herein. As a database we recorded 2k dialog turns directed to an automotive infotainment interface during extensive usability studies. Speech recognition and natural language interpretation have thereby been realized once as a Wizard-of-Oz simulation, and once by actual recognition technology. Recorded utterances have been labelled using a closed set of four emotions, namely anger, confusion, joy, and neutrality. As acoustic features we apply a high number of prosodic, speech quality, and articulatory functionals derived by descriptive statistic analysis out of base contours as intonation, intensity, and spectral information. Self-learning feature generation and selection is employed to optimize complexity for the successive classification by Support Vector Machines. Semantic information is included by vector-space representation of the spoken content within an early feature fusion. Overall, high recognition performances can be reported for this task by the suggested approach.

Áreas y palabras clave

Cómo citar (APA 7)

Schuller, B. W., Lang, M., & Rigoll, G. (2006). Recognition of Spontaneous Emotions by Speech within Automotive Environment. Augsburg University. https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/76732