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

Abstract

Boosted by a wide potential application spectrum, emotional speech recognition, i.e., the automatic computer-aided 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 real-world scenarios, for example, robustness, real-time capability, and realistic speech corpora. Motivated by these facts, a robust, low-complex 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 bag-of-words 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 2-class paradigm negative versus non-negative emotional states attests to the potential of bags-of-words in speech emotion recognition in the wild.