  Multilingual speech processing requires understanding emotions, a task made
difficult by limited labelled data. CLARA, minimizes reliance on labelled data,
enhancing generalization across languages. It excels at fostering shared
representations, aiding cross-lingual transfer of speech and emotions, even
with little data. Our approach adeptly captures emotional nuances in speech,
overcoming subjective assessment issues. Using a large multilingual audio
corpus and self-supervised learning, CLARA develops speech representations
enriched with emotions, advancing emotion-aware multilingual speech processing.
  Our method expands the data range using data augmentation, textual embedding
for visual understanding, and transfers knowledge from high- to low-resource
languages. CLARA demonstrates excellent performance in emotion recognition,
language comprehension, and audio benchmarks, excelling in zero-shot and
few-shot learning. It adapts to low-resource languages, marking progress in
multilingual speech representation learning.
