  In this paper, we analyze the feasibility of applying few-shot learning to
speech emotion recognition task (SER). The current speech emotion recognition
models work exceptionally well but fail when then input is multilingual.
Moreover, when training such models, the models' performance is suitable only
when the training corpus is vast. This availability of a big training corpus is
a significant problem when choosing a language that is not much popular or
obscure. We attempt to solve this challenge of multilingualism and lack of
available data by turning this problem into a few-shot learning problem. We
suggest relaxing the assumption that all N classes in an N-way K-shot problem
be new and define an N+F way problem where N and F are the number of emotion
classes and predefined fixed classes, respectively. We propose this
modification to the Model-Agnostic MetaLearning (MAML) algorithm to solve the
problem and call this new model F-MAML. This modification performs better than
the original MAML and outperforms on EmoFilm dataset.
