  There is an imminent need for guidelines and standard test sets to allow
direct and fair comparisons of speech emotion recognition (SER). While
resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP)
database, have emerged as widely-adopted reference corpora for researchers to
develop and test models for SER, published work reveals a wide range of
assumptions and variety in its use that challenge reproducibility and
generalization. Based on a critical review of the latest advances in SER using
IEMOCAP as the use case, our work aims at two contributions: First, using an
analysis of the recent literature, including assumptions made and metrics used
therein, we provide a set of SER evaluation guidelines. Second, using recent
publications with open-sourced implementations, we focus on reproducibility
assessment in SER.

Sound;Machine Learning;Audio and Speech Processing