  Recent developments in speech emotion recognition (SER) often leverage deep
neural networks (DNNs). Comparing and benchmarking different DNN models can
often be tedious due to the use of different datasets and evaluation protocols.
To facilitate the process, here, we present the Speech Emotion Recognition
Adaptation Benchmark (SERAB), a framework for evaluating the performance and
generalization capacity of different approaches for utterance-level SER. The
benchmark is composed of nine datasets for SER in six languages. Since the
datasets have different sizes and numbers of emotional classes, the proposed
setup is particularly suitable for estimating the generalization capacity of
pre-trained DNN-based feature extractors. We used the proposed framework to
evaluate a selection of standard hand-crafted feature sets and state-of-the-art
DNN representations. The results highlight that using only a subset of the data
included in SERAB can result in biased evaluation, while compliance with the
proposed protocol can circumvent this issue.
