Speech Emotion Recognition with DNN and Combination of CNN-LSTM

Emotion is essential to all living things. Understanding emotion is challenging for everyone, but it can solve thousands of problems and save many lives if done correctly. Emotion is represented in gestures, facial expression, speech, etc. Recording of speech is easy with non-invasive nature of processing. As a result, throughout the past three decades, numerous researchers have been interested in emotion recognition through speech. In this study, We address the crucial task of speech emotion recognition (SER) by leveraging deep learning architectures, namely Convolutional Neural Networks (CNN) combined with Long Short-Term Memory (LSTM) layers and Deep Neural Networks (DNN). our approach utilises a diverse feature set, including Mel spectrogram, MFCCs, spectrogram, delta of MFCCs, chroma feature, and tonal centroid features extracted from the EmoDB dataset. Through extensive exper-imentation employing a 5-fold cross-validation methodology, we achieved notable classification accuracies of 77.38% with the CNN+LSTM model and 87.09% with the DNN model.