No abstract

We investigate, for the first time, if applying model selection based on automatic language identification (LID) can improve multilingual recognition of emotion in speech. Six emotional speech corpora from three language families (Germanic, Romance, Sino-Tibetan) are evaluated. The emotions are represented by the quadrants in the arousal/valence plane, i. e., positive/negative arousal/valence. Four selection approaches for choosing an optimal training set depending on the current language are compared: within the same language family, across language family, use of all available corpora, and selection based on the automatic LID. We found that, on average, the proposed LID approach for selecting training corpora is superior to using all the available corpora when the spoken language is not known.

We propose a novel method to learn multiscale kernels with locally penalised discriminant analysis, namely Multiscale-Kernel Locally Penalised Discriminant Analysis (MS-KLPD A). As an exemplary use-case, we apply it to recognise emotions in speech. Specifically, we employ the term of locally penalised discriminant analysis by controlling the weights of marginal sample pairs, while the method learns kernels with multiple scales. Evaluated in a series of experiments on emotional speech corpora, our proposed MS-KLPDA is able to outperform the previous research of Multiscale-Kernel Fisher Discriminant Analysis and some conventional methods in solving speech emotion recognition. CCS Concepts•Information systems → Multimedia information systems; •Human-centered computing → HCI theory, concepts and models; •Theory of computation → Kernel methods;