Speech Emotion Recognition Using CNN and LSTM- A Review of Literature

computer engineering

Emotion recognition is always a very tough job, particularly if we are recognizing an emotion by using a speech signal. Many remarkable research works have been done on emotion recognition using speech signals. The primary challenges of emotion recognition are choosing the emotion recognition corpora (speech database), identification of different features related to speech, and an appropriate choice of a classification model. In this article, we use 13 MFCC (Mel Frequency Cepstral Coefficient) with 13 velocity and 13 acceleration components as features and a CNN (Convolution Neural Network) and LSTM (Long Short-Term Memory) based approach for classification. We chose Berlin Emotional Speech dataset (EmoDB) for classification purposes. We have approximately 80 percent of accuracy on test data.

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