Automatic Speech Emotion Recognition (SER) is used to recognize emotion from speech automatically. Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender, age, the cultural and acoustical background of the speaker. The acoustical resemblance between emotional expressions further increases the complexity of recognition. Many recent research works are concentrated to address these effects individually. Instead of addressing every influencing attribute individually, we would like to design a system, which reduces the effect that arises on any factor. We propose a two-level Hierarchical classifier named Interpreter of responses (IR). The first level of IR has been realized using Support Vector Machine (SVM) and Gaussian Mixer Model (GMM) classifiers. In the second level of IR, a discriminative SVM classifier has been trained and tested with meta information of first-level classifiers along with the input acoustical feature vector which is used in primary classifiers. To train the system with a corpus of versatile nature, an integrated emotion corpus has been composed using emotion samples of 5 speech corpora, namely; EMO-DB, IITKGP-SESC, SAVEE Corpus, Spanish emotion corpus, CMU's Woogle corpus. The hierarchical classifier has been trained and tested using MFCC and Low-Level Descriptors (LLD). The empirical analysis shows that the proposed classifier outperforms the traditional classifiers. The proposed ensemble design is very generic and can be adapted even when the number and nature of features change. The first-level classifiers GMM or SVM may be replaced with any other learning algorithm.

Recognition of emotion in speech is a difficult task due to many speaker factors like gender, age, and the cultural background (nationality, ethnicity, and region) as well as the acoustical environment. Among these factors, the cultural background of the speaker has a strong influence on the expression of emotion. The reason for the unsatisfactory performance of an emotion recognition engine built using mixed-cultural samples can be traced back to this. To address this issue, a two-level hierarchical engine has been designed to identify emotion from the speech of different cultural backgrounds. The first level of the hierarchical engine is a culture identification system, which identifies the corpus of an input utterance. As most of the speakers involved in the construction of a specific corpus are from the same locality and cultural background, we assume that a corpus represents the cultural background of the speakers of the corpus constructed. Based on the response of the first level classifier, the input utterance is forwarded to an appropriate corpus-specific emotion recognition engine, in the second level. Each corpus-specific emotion recognition system is a discriminative, multiclass SVM classifier, trained with the emotional utterances of that particular corpus. The system has been tested with five different corpora, collected from diverse cultural backgrounds, namely EMO-DB, SAVEE, IITKGP-SEC, Spanish corpus S0329, and CMU’s Woogles corpus. The system achieved an accuracy of 82.01% which is an improvement of 13.38% over monolithic approaches.

The learning community’s size increases daily along with the population. Thus, the manual evaluation became a cumbersome and tedious process to evaluate a The learning community’s size increases daily along with the population. Thus, the manual evaluation became a cumbersome and tedious process to evaluate a