  A language agnostic approach to recognizing emotions from speech remains an
incomplete and challenging task. In this paper, we performed a step-by-step
comparative analysis of Speech Emotion Recognition (SER) using Bangla and
English languages to assess whether distinguishing emotions from speech is
independent of language. Six emotions were categorized for this study, such as
- happy, angry, neutral, sad, disgust, and fear. We employed three Emotional
Speech Sets (ESS), of which the first two were developed by native Bengali
speakers in Bangla and English languages separately. The third was a subset of
the Toronto Emotional Speech Set (TESS), which was developed by native English
speakers from Canada. We carefully selected language-independent prosodic
features, adopted a Support Vector Machine (SVM) model, and conducted three
experiments to carry out our proposition. In the first experiment, we measured
the performance of the three speech sets individually, followed by the second
experiment, where different ESS pairs were integrated to analyze the impact on
SER. Finally, we measured the recognition rate by training and testing the
model with different speech sets in the third experiment. Although this study
reveals that SER in Bangla and English languages is mostly
language-independent, some disparities were observed while recognizing
emotional states like disgust and fear in these two languages. Moreover, our
investigations revealed that non-native speakers convey emotions through
speech, much like expressing themselves in their native tongue.
