Abstract:
Use of the error correcting codes (ECC) in a multiclass audio emotion recognition problem is proposed to improve the emotion recognition accuracy. We visualize the emotio...Show MoreMetadata
Abstract:
Use of the error correcting codes (ECC) in a multiclass audio emotion recognition problem is proposed to improve the emotion recognition accuracy. We visualize the emotion recognition system as a noisy communication channel, thus motivating the use of ECC. We assume the emotion recognition process consists of an audio feature extractor followed by an artificial neural network (ANN) for emotion classification. In our formulation, the noise in the communication channel is a result of insufficiently learnt ANN classifier which results in an erroneous emotion classification. We first show that the ECC-ANN combination performs better than the ANN classifier, justifying the use of ECC-ANN combination. We further make the conjecture that ECC in ECC-ANN combination can be visualized as a part of Deep Neural Network (DNN) where the intelligence is under control. We show through rigorous experimentation, on Emo-DB database, that the use of ECC-ANN combination is equivalent to the DNN; in terms of the improved recognition accuracies over an ANN. Our experimental results show that both ECC-ANN and DNN give a minimum absolute improvement of around 13.75%.
Date of Conference: 09-12 October 2016
Date Added to IEEE Xplore: 09 February 2017
ISBN Information: