Semantic Similarity Effects on Zero-Shot Transfer Accuracy in Low-Resource Languages on XTREME-R

Abstract

Abstract: Speech Affect Recognition is a problem of extracting emotional affects from audio data. Low resource languages corpora are rear and affect recognition is a difficult task in cross-corpus settings. We present an approach in which the model is trained on high resource language and fine-tune to recognize affects in low resource language. We train the model in same corpus setting on SAVEE, EMOVO, Urdu, and IEMOCAP by achieving baseline accuracy of 60.45, 68.05, 80.34, and 56.58 percent respectively. For capturing the diversity of affects in languages cross-corpus evaluations are discussed in detai

Research Question

How does semantic similarity between intermediate and target tasks in low-resource languages affect zero-shot transfer accuracy on XTREME-R compared to high-resource language intermediates?

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