Decoding Emotions: A comprehensive Multilingual Study of Speech Models
  for Speech Emotion Recognition
Anant Singh Akshat Gupta
Recent advancements in transformer-based speech representation models have
greatly transformed speech processing. However, there has been limited research
conducted on evaluating these models for speech emotion recognition (SER)
across multiple languages and examining their internal representations. This
article addresses these gaps by presenting a comprehensive benchmark for SER
with eight speech representation models and six different languages. We
conducted probing experiments to gain insights into inner workings of these
models for SER. We find that using features from a single optimal layer of a
speech model reduces the error rate by 32\% on average across seven datasets
when compared to systems where features from all layers of speech models are
used. We also achieve state-of-the-art results for German and Persian
languages. Our probing results indicate that the middle layers of speech models
capture the most important emotional information for speech emotion
recognition.