Cross-language speech emotion recognition is receiving increased attention due to its extensive real-world applicability. This work proposes a language-agnostic speech emotion recognition algorithm focusing on Italian and German languages. The mel-scaled and temporal modulation spectral representations are combined and then subsequently modeled by means of Gaussian mixture models. Emotion prediction is carried out via a Kullback Leibler divergence scheme. The proposed methodology is applied to two problem settings: one including positive vs. negative emotion classification and a second one where all Big Six emotional states are considered. A thorough experimental campaign demonstrated the efficacy of such a method, as well as its superiority over other generative modeling schemes and state-of-the-art approaches. The results demonstrate the feasibility of recognizing emotional states in a language-, gender- and speaker-independent setting.
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