Carlos Busso

TL;DR: A new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory at the University of Southern California (USC), which provides detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios.

TL;DR: A basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis, is proposed and intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters.

TL;DR: Results reveal that the system based on facial expression gave better performance than the systembased on just acoustic information for the emotions considered, and that when these two modalities are fused, the performance and the robustness of the emotion recognition system improve measurably.

TL;DR: A hierarchical computational structure to recognize emotions is introduced that maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications and is effective for classifying emotional utterances in multiple database contexts.

TL;DR: In this article, a hierarchical computational structure is proposed to recognize emotions, which maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications.