Detection of negative emotions in speech signals using bags-of-audio-words

Boosted by a wide potential application spectrum, emotional speech recognition, i. e., the automatic computeraided identification of human emotional states based on speech signals, currently describes a popular field of research. However, a variety of studies especially concentrating on the recognition of negative emotions often neglected the specific requirements of realworld scenarios, for example, robustness, realtime capability, and realistic speech corpora. Motivated by these facts, a robust, lowcomplex classification system for the detection of negative emotions in speech signals was implemented on the basis of a spontaneous, strongly emotionally colored speech corpus. Therefore, an innovative approach in the field of emotion recognition was applied as the core of the system – the bagofwords approach that is originally known from text and image document retrieval applications. Thorough performance evaluations were carried out and a promising recognition accuracy of 65.6% for the 2class paradigm negative versus nonnegative emotional states attests to the potential of bagsofwords in speech emotion recognition in the wild.