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新论文 · v1arXiv:2611.15691

Dynamic Block-Online Streaming ASR for Low-Resource Agglutinative Code-Switching Speech with Morphology-Aware Evaluation

Kazi Rafat, Afifa Imran, Md. Ismail Hossain, Md. Romzan Ali, Fuad Rahman, Sifat Momen, Shafin Rahman, Nabeel Mohammed

原文摘要

Low-resource, agglutinative languages challenge low-latency ASR, as fixed lookahead windows fail when grapheme order diverges, suffixes cross code-switch boundaries, or code-switching data is imbalanced. We present the first comprehensive study of streaming ASR for intra-sentential Bangla-English code-switching, identifying a mismatch between strict causal streaming and long-range morphological dependencies. We introduce a Dynamic Block-Online framework with VAD-aligned inference that balances semantics, latency, and context, enabling global bidirectional attention to preserve morphological integrity. To mitigate data scarcity, we propose Script-Anchored Loanword Injection, augmenting training text with English loanwords to model phonotactic transitions. We introduce CS-WER to diagnose switch, root, and suffix errors, and show that flexible latency budgets enhance recognition of agglutinative and mixed-language speech, with successful transfer to menstrual-medical domains.

cs.CL · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.23335

The Gate Always Closes: On Injecting Auxiliary Signals into Frozen Vision-Language Models

Moshiur Farazi, Sameera Ramasinghe, Bekir Sait Ciftler, Mahbub Ahmed Turza, Shafin Rahman

原文摘要

Auxiliary signal pathways in VLMs are routinely fitted with learnable gates so the optimiser can decide how much of the signal to admit. We study what the optimiser decides, and whether the model then uses the signal, with hyperbolic visual relational graphs as the experimental vehicle. We find that under cross-entropy fine-tuning the optimiser decides on zero: the gate closes in every design we trained, across five injection designs on LLaVA-1.5-7B, 15 of 15 runs on Qwen2-VL-7B spanning four task families, 3 of 3 seeds on InternVL3-8B, and with LoRA on all linear layers. We attribute this suppression phenomenon to two regimes, a dead-gradient regime formalised through the caption-invariance of image-derived signals and measured directly, and a negative-utility regime in which the auxiliary signal actively hurts the loss. A closed gate does not by itself mean an inert pathway, so we ablate each pathway at inference under one protocol with real region proposals: the norm-aligned designs are behaviourally closed (at most 1.5pp change at the learned gate), whereas the un-normalised gated prefix is open and harmful, costing 6.7pp on relational GQA at its learned gate of +0.005 while helping SugarCrepe. Evaluating every configuration under one inference mode shows that an active injected pathway costs up to 15pp on relational GQA and 7-12pp on out-of-distribution VSR, that the geometric training losses give no measurable in-distribution gain once the pathway is dropped, and that a Euclidean containment control ties the hyperbolic one. Embedding-norm alignment remains necessary for prefix injection that preserves generation. We recommend testing signal use by ablation rather than by reading gate values or attention mass. We open-source the implementation, the checkpoints and the result files behind every table at https://github.com/mrfarazi/the-gate-always-closes.

cs.CV

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arXiv ↗PDF ↗