Emotion Detection from Speech – Thesis successfully defended


The Bachelor Thesis deals with research in the field of emotion recognition mainly from speech and marginally from other modalities (video and physiological data).

It closely describes the topology of the systems built specifically for the subject of this work. Moreover, it describes experiments leading to optimized pre-processing, regressor training and post-processing. Data used for these research origins from evaluation AV+EC 2015.

Results of fusion systems producing the most precise prediction were sent to this evaluation. The Bottle-Neck features are newly tested and combined favorably with commonly used eGeMAPS features for the recognition of arousal.

For valence, two kinds of video features are used. Muli-task system (recognizing both valence and arousal) using Bottle-Neck features produces competitive results and is only 13 % relatively behind the mentioned fusion system. This is especially appealing for applications where only audio is available.

The Bachelor Thesis was successful defended by Anna Popkova at Brno University of Technology (BUT) in June 2016.