Prominence features: Effective emotional features for speech emotion recognition

Emotion-related feature extraction is a challenging task in speech emotion recognition. Due to the lack of discriminative acoustic features, classical approaches based on traditional acoustic features could not provide satisfactory performances. This research proposes a novel type of feature related to prominence, which, together with traditional acoustic features, are used to classify seven typical different emotional states. To this end, the author group produces a Chinese Dual-mode Emotional Speech Database (CDESD), which contains additional prominence and paralinguistic annotation information. Then, a consistency assessment algorithm is presented to validate the reliability of the annotation information of this database. The results show that the annotation consistency on prominence reaches more than 60% on average. Subsequently, this research analyzes the correlation of the prominence features with emotional states using a curve fitting method. Prominence is found to be closely related to emotion states, to retain emotional information at the word level to the greatest possible extent and to play an important role in emotional expression. Finally, the proposed prominence features are validated on CDESD through speaker dependent and speaker-independent experiments with four commonly used classifiers. The results show that the average recognition rate achieved using the combined features is improved by 6% in speaker dependent experiments and by 6.2% in speaker-independent experiments compared with that achieved using only acoustic features. (C) 2017 Elsevier Inc. All rights reserved.