Automatic Emotion Recognition from Cochlear Implant-Like Spectrally Reduced Speech

Abstract

In this paper we present a robust feature extractor that includes the In this paper we study the performance of emotion recognition from cochlear implant- like spectrally reduced speech (SRS) using the conventional Melfrequency cepstral coefficients and a Gaussian mixture model (GMM)-based classifier. Cochlear-implant-like SRS of each utterance from the emotional speech corpus is obtained only from low-bandwidth subband temporal envelopes of the corresponding original utterance. The resulting utterances have less spectral information than the original utterances but contain the most relevant information for emotion recognition. The emotion classes are trained on the Mel-frequency cepstral coefficient (MFCC) features extracted from the SRS signals and classification is performed using MFCC features computed from the test SRS signals. In order to evaluate to the performance of the SRS-MFCC features, emotion recognition experiments are conducted on the FAU AIBO spontaneous emotion corpus. Conventional MFCC, Mel-warped DFT (discrete Fourier transform) spectrum-based cepstral coefficients (MWDCC), PLP (perceptual linear prediction), and amplitude modulation cepstral coefficient (AMCC) features extracted from the original signals are used for comparison purpose. Experimental results depict that the SRS-MFCC features outperformed all other features in terms of emotion recognition accuracy. Average relative improvements obtained over all baseline systems are 1.5% and 11.6% in terms of unweighted average recall and weighted average recall, respectively.