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Human emotion detection from multiple languages is a very challenging job. In this work, we have used language emotional databases of various languages such as – Ryerson-Audio-Visual database (RAVDESS), Berlin Database (EmoDb) and Italian Database (Emo-Vo) which are in English, German and Italian languages respectively. The proposed model extract MFCC, chroma, Tonnetz, Contrast from the raw audio file, which is further taken as input in the CNN model to identify emotions correctly. We are not using any visual representation of sound only direct from natural sound data. An extensive comparison is made with some of the previous approaches on emotion detection from speech. The experimental result shows that; the proposed model has successfully worked with all the selected databases with higher accuracy. The same also has been tested with the augmented database. We secure 70.46% for RAVDESS, 70.37% Emo-Db and 73.47% for Emo-Vo in the initial database and best model work in the augmented database. However, test with Original test dataset, secured 96.53% in RAVDESS 96.22% in Emo-Db and Emo-Vo 96.11% respectively. Multilingual Emotion detection, a state of art model, has been discussed with an accuracy of 97.89%. The proposed model is a speaker-independent as well as language-independent emotion detection system. [ABSTRACT FROM AUTHOR]

This paper focusses on investigation of the effective performance of perceptual based speech features on emotion detection. Mel frequency cepstral coefficients (MFCC's), perceptual linear predictive cepstrum (PLPC), Mel frequency perceptual linear prediction cepstrum (MFPLPC), bark frequency cepstral coefficients (BFCC), revised perceptual linear prediction coefficient's (RPLP) and inverted Mel frequency cepstral coefficients (IMFCC) are the perception features considered. The algorithm using these auditory cues is evaluated with deep neural networks (DNN). The novelty of the work involves analysis of the perceptual features to identify predominant features that contain significant emotional information about the speaker. The validity of the algorithm is analysed on publicly available Berlin database with seven emotions in 1-dimensional space termed categorical and 2-dimensional continuous space consisting of emotions in valence and arousal dimensions. Comparative analysis reveals that considerable improvement in the performance of emotion recognition is obtained using DNN with the identified combination of perceptual features. [ABSTRACT FROM AUTHOR]