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This paper evaluates the Mamba state space model for ASR on seven South African languages, finding it matches Conformer accuracy with fewer resources, and explores multilingual training strategies and low-resource settings.
This paper investigates whether code-switching ASR capabilities learned from limited seen language pairs can generalize to unseen pairs using model merging and domain generalization methods, finding only modest transfer.
A routing-based approach for real-time multilingual ASR that uses smaller monolingual models with a rollback mechanism to handle language switches, achieving ~13% WER on inter-utterance code-switching and open-sourcing the system.
This paper applies Direct Preference Optimization (DPO) to align Audio LLMs for transcribing English-Mandarin code-switching speech, achieving up to 89.6% MER reduction in-distribution and 20% out-of-distribution. It identifies three failure modes—language omission, translation instead of transcription, and hallucination—and shows that preference-based alignment effectively elicits correct code-switching behavior from multilingual Audio LLMs.
Introduces Vividh-ASR, a complexity-tiered benchmark for Hindi and Malayalam ASR, identifies studio-bias in fine-tuning, and proposes R-MFT to improve spontaneous speech performance efficiently.
MUSCAT is a new multilingual, scientific conversation benchmark dataset for evaluating ASR systems on challenging multilingual scenarios including code-switching, domain-specific vocabulary, and mixed language input. The dataset consists of bilingual discussions on scientific papers between speakers using different languages, with results showing current state-of-the-art systems struggle with these multilingual challenges.