Speaker dependent, speaker independent and cross language emotion recognition from speech using GMM and HMM

In this paper we have analysed emotion recognition performance in speaker dependent, text dependent, text independent, speaker independent, language dependent and cross language emotion recognition from speech. These studies were carried out using Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) as classification models. IITKGP-SESC and IITKGP-SEHSC emotional speech corpora are used for carried out these studies. The emotions considered in this study are anger, disgust, fear, happy, neutral, sarcastic, and surprise. Mel Frequency Cepstral Coefficients (MFCCs) features are used for identifying the emotions. Emotion recognition performance of speaker dependent mode is better than speaker independent and cross language modes. From the results it is observed that emotion recognition performance depends on the speaker and language.