An Analysis of the Impact of Spectral Contrast Feature in Speech Emotion Recognition

Shreya Kumar, Swarnalaxmi Thiruvenkadam


Feature extraction is an integral part in speech emotion recognition. Some emotions become indistinguishable from others due to high resemblance in their features, which results in low prediction accuracy. This paper analyses the impact of spectral contrast feature in increasing the accuracy for such emotions. The RAVDESS dataset has been chosen for this study. The SAVEE dataset, CREMA-D dataset and JL corpus dataset were also used to test its performance over different English accents. In addition to that, EmoDB dataset has been used to study its performance in the German language. The use of spectral contrast feature has increased the prediction accuracy in speech emotion recognition systems to a good degree as it performs well in distinguishing emotions with significant differences in arousal levels, and it has been discussed in detail. 


Chroma; Feature extraction; Mel Frequency Cepstral coefficients; Multi-layer perceptron; Spectral contrast

Full Text:


International Journal of Recent Contributions from Engineering, Science & IT (iJES) – eISSN: 2197-8581
Creative Commons License
DOAJ logo DBLP logo MAS logo