Mood detection from daily conversational speech using denoising autoencoder and LSTM

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 5125 - 5129
• Main Authors: Kun-Yi Huang, Chung-Hsien Wu, Ming-Hsiang Su, Hsiang-Chi Fu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2017
• Subjects: denoising autoencoder; Emotion recognition; Hidden Markov models; long short-term memory; Long-term emotion tracking; Mood; mood detection; Predictive models; Speech; Support vector machines
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7953133

In current studies, an extended subjective self-report method is generally used for measuring emotions. Even though it is commonly accepted that speech emotion perceived by the listener is close to the intended emotion conveyed by the speaker, research has indicated that there still remains a mismatch between them. In addition, the individuals with different personalities generally have different emotion expressions. Based on the investigation, in this study, a support vector machine (SVM)-based emotion model is first developed to detect perceived emotion from daily conversational speech. Then, a denoising autoencoder (DAE) is used to construct an emotion conversion model to characterize the relationship between the perceived emotion and the expressed emotion of the subject for a specific personality. Finally, a long short-term memory (LSTM)-based mood model is constructed to model the temporal fluctuation of speech emotions for mood detection. Experimental results show that the proposed method achieved a detection accuracy of 64.5%, improving by 5.0% compared to the HMM-based method.