Abstract
The most common form of communication is speech. Speech Emotion Recognition is a demanding study area that aims to identify human emotions from speech information (SER). Capturing the emotion from only speech is a difficult challenge. The proper selection of features in relation with both the time and frequency domains together is necessary to produce optimized results. In this paper, four emotional states: Happy, Sad, Anger and Neutral from speech are recognized by using two classifiers. The speech utterances are taken from the Poland Corpus (Database of Polish Emotional Speech). The explored features include Energy, Pitch, Zero-crossing rate and Mel-Frequency Cepstrum Coefficients (MFCC). Performance is compared by employing two different classification algorithms namely, Support vector machine (SVMs) and Linear Discriminant Analysis (LDA). The experimental results reveal that SVM offers15% relative accuracy improvement compared to LDA.
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Indra, J., Shankar, R.K., Priya, R.D. (2023). Speech Emotion Recognition Using Support Vector Machine and Linear Discriminant Analysis. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_47
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