The real challenge in human-computer interaction is understanding human emotions by machines and responding to it accordingly. Emotion varies by gender and age of the speaker, location, and cause. This article focuses on the improvement of emotion recognition (ER) from speech using gender-biased influences in emotional expression. The problem is addressed by testing emotional speech with an appropriate specific-gender ER system. As acoustical characteristics vary among the genders, there may not be a common optimal feature set across both genders. Gender-based speech emotion recognition, a two-level hierarchical ER system is proposed, where the first level is gender identification which identifies the gender, and the second level is a gender-specific ER system, trained with an optimal feature set of expressions of a particular gender. The proposed system increases the accuracy of traditional Speech Emotion Recognition Systems (SER) by 10.36% than the SER trained with mixed gender training when tested on the EMO-DB Corpus.
The Role of Task and Acoustic Similarity in Audio Transfer Learning: Insights from the Speech Emotion Recognition Case