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This paper introduces the Semantic Gambit attack, which uses LLM predictions to generate real-time adversarial perturbations for automatic speech recognition systems, achieving a three-fold increase in word error rate over prior methods.
This paper introduces Agentic ASR, an interactive speech recognition framework that uses semantic correction and reasoning-based editing to reduce semantic errors through multi-turn refinement. It also proposes a new sentence-level semantic error rate metric and an interactive simulation system for benchmarking.
SCRIBE is a diagnostic evaluation framework for automatic speech recognition that provides categorical error decomposition for Indic languages, releasing benchmarks and open-weight rich transcription models for Hindi, Malayalam, and Kannada.
FormalASR presents two compact end-to-end models that directly transcribe spoken Chinese into formal written text, achieving significant error reduction and eliminating the need for a separate LLM post-processing stage, enabling lightweight on-device deployment.
NVIDIA releases Nemotron 3.5 ASR, a 600M parameter multilingual streaming speech recognition model supporting 40 language-locales with a Cache-Aware FastConformer-RNNT architecture for low-latency transcription. The model supports configurable chunk sizes and is ready for commercial use under the OpenMDW-1.1 license.
This paper presents Flavors of Moonshine, a suite of tiny specialized ASR models for edge devices. The authors show that monolingual models trained on a balanced mix of human-labeled, pseudo-labeled, and synthetic data outperform larger multilingual models like Whisper, achieving state-of-the-art error rates for small models and enabling on-device ASR for underrepresented languages.