In this paper we present an innovative approach for utterance-level emotion recognition by fusing acoustic features with lexical features extracted from automatic speech recognition (ASR) output. The acoustic features are generated by combining: (1) a novel set of features that are derived from segmental Mel Frequency Cepstral Coefficients (MFCC) scored against emotion-dependent Gaussian mixture models, and (2) statistical functionals of low-level feature descriptors such as intensity, fundamental frequency, jitter, shimmer, etc. These acoustic features are fused with two types of lexical features extracted from the ASR output: (1) presence/absence of word stems, and (2) bag-of-words sentiment categories. The combined feature set is used to train support vector machines (SVM) for emotion classification. We demonstrate the efficacy of our approach by performing four-way emotion recognition on the University of Southern California's Interactive Emotional Motion Capture (USC-IEMOCAP) corpus. Our experiments show that the fusion of acoustic and lexical features delivers an emotion recognition accuracy of 65.7%, outperforming the previously reported best results on this challenging dataset.
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