Towards robust speech-based emotion recognition

Maintaining the robustness of a speech processing system in the presence of noise is a challenge. This paper shows frequency subband architecture can improve robustness of an emotion recognition system when signals are corrupted by selective noise. This paper also demonstrates that feature selection based on some mutual-information criterion (maximum-relevant minimum-redundancy) can give us the most effective subset of features to get a better result.