Do multilingual audio representation models demonstrate greater robustness to domain shift in code-switched sp

Abstract

Abstract: Pre-trained models for automatic speech recognition (ASR) and speech enhancement (SE) have exhibited remarkable capabilities under matched noise and channel conditions. However, these models often suffer from severe performance degradation when confronted with domain shifts, particularly in the presence of unseen noise and channel distortions. In view of this, we in this paper present URSA-GAN, a unified and domain-aware generative framework specifically designed to mitigate mismatches in both noise and channel conditions. URSA-GAN leverages a dual-embedding architecture that consists of a noi

Research Question

Do multilingual audio representation models demonstrate greater robustness to domain shift in code-switched speech recognition tasks than monolingual counterparts when evaluated on standard speech benchmarks?

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