Phase-based Cepstral features for Automatic Speech Emotion Recognition of Low Resource Indian languages

Automatic speech emotion recognition (SER) is a crucial task in communication-based systems, where feature extraction plays an important role. Recently, a lot of SER models have been developed and implemented successfully in English and other western languages. However, the performance of the traditional Indian languages in SER is not up to the mark. This problem of SER in low-resource Indian languages mainly the Bengali language is dealt with in this paper. In the first step, the relevant phase-based information from the speech signal is extracted in the form of phase-based cepstral features (PBCC) using cepstral, and statistical analysis. Several pre-processing techniques are combined with features extraction and gradient boosting machine-based classifier in the proposed SER model. Finally, the evaluation and comparison of simulation results on speaker-dependent, speaker-independent tests are performed using multiple language datasets, and independent test sets. It is observed that the proposed PBCC features-based model is performing well with an average of 96% emotion recognition efficiency as compared to standard methods.