Improved multi-lingual sentiment analysis and recognition using deep learning

Speech emotion recognition (SER) is still a fresh in natural language processing domain since the accuracy is beyond targeted. Mainly due to real-time applications such as human–robot interaction, human behaviour evaluation and virtual reality rely heavily on SER. Moreover, cross-lingual SER plays a significant role in practical applications, especially when users of different cultural and linguistic backgrounds interact with the system. However, the existing conventional approaches of SER cannot be employed for real-world applications because it uses the same corpus for training and testing, which cannot be used for multi-lingual environments to detect or classify real emotions. In such a situation, the performance of SER is degraded. Therefore, the proposed work develops cross-lingual emotion recognition through Urdu, Italian, English and German. The features are extracted through the most employed audio feature known as MFCCs (Mel Frequency Cepstral Coefficients). Experimental results exhibited that the proposed deep learning model comes out with promising results on the URDU data set with 91.25% accuracy using random forest (RF) and XGBoost classifier.