Cepstral and long-term features for emotion recognition
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Systems that were developed for the Open Performance Sub-Challenge of the INTERSPEECH 2009 Emotion Challenge are described, which participate in both two-class and fiveclass emotion detection.
Abstract
In this paper, we describe systems that were developed for the Open Performance Sub-Challenge of the INTERSPEECH 2009 Emotion Challenge. We participate in both two-class and fiveclass emotion detection. For the two-class problem, the best performance is obtained by logistic regression fusion of three systems. These systems use short- and long-term speech features. Fusion allowed to an absolute improvement of 2:6% on the unweighted recall value compared with [1]. For the fiveclass problem, we submitted two individual systems: cepstral GMM vs. long-term GMM-UBM. The best result comes from a cepstral GMM and produces an absolute improvement of 3:5% compared to [6].