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

SERAB: A multi-lingual benchmark for speech emotion recognition