Improved speech emotion recognition using error correcting codes

We propose the use of the popular error correcting codes (ECC) in a multi-class audio emotion recognition scenario to improve the emotion recognition accuracy in spoken speech. In this paper, we visualize the emotion recognition system as a noisy communication channel, thus motivating the use of ECC in the emotion recognition process. We assume the emotion recognition process consists of an audio feature extraction module followed by an artificial neural network (ANN) for emotion (represented by a binary string) classification. The noisy communication channel, in our formulation, is the insufficiently learnt ANN classifier which in turn results in an erroneous (binary string) emotion classification. In our system, we use ECC to encode the binary string representing the emotion class using a Block Coder (BC). We show through rigorous experimentation, on Emo-DB database, that the use of ECC improves the recognition accuracy of the emotion classification system in the range of (4.6 - 9.35)% in comparison to the baseline ANN-based emotion classification system.

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