A new arXiv paper examines the internal representations of Whisper, an automatic speech recognition model, by applying sparse autoencoders to its encodings. The authors note that interpretability research has focused mostly on text-based transformers, leaving speech systems comparatively unstudied. Their work aims to make the features learned by Whisper's encoder more understandable.
A study argues that one-shot magnitude pruning does more than compress neural networks, acting as an implicit regularizer for automatic speech recognition. Testing with Whisper-small, the authors combine gradient- and Fisher-based sensitivity measures to guide which weights to remove. The work reframes pruning as a training technique rather than only a efficiency tool.
A new arXiv paper examines how well audio-language models can turn long doctor-patient conversations into structured SOAP clinical notes. The authors compare lightweight and heavyweight end-to-end approaches, noting that while cascaded speech recognition pipelines remain strong, end-to-end models tend to lose information or produce hallucinations. The work targets the modality gap in long-form clinical audio.
A new arXiv paper introduces VoiceCodeBench, a benchmark that checks whether speech recognition transcripts reproduce exact written values rather than just scoring well on word error rate. It focuses on structured tokens such as identifiers, file paths, and commands, which voice-driven workflows often need verbatim. The work argues that WER alone does not capture whether these exact tokens survive transcription.
A research team reports on a multi-month engineering effort to build Sophea, a bilingual Greek-English automatic speech recognition system intended for production use. The system was assessed against nine production gates, including word error rates for both languages and language identification performance. The work is presented as a case study in the engineering work required to move speech recognition from research to deployment.
A new arXiv paper introduces RetroThinker, a method that lets speech large language models revisit and revise earlier reasoning steps. Speech LLMs cut latency and preserve vocal cues lost in pipelines that combine speech recognition with text models, but they still trail text-only systems in capability. The work aims to narrow that gap by adapting retrospective reasoning to the speech setting.
A new arXiv paper introduces BuzzASR, a collection of more than one hundred monolingual Whisper models fine-tuned for automatic speech recognition in 102 languages. The language-specialized models are designed to cover languages that large general-purpose multilingual ASR systems often serve poorly. The release is presented as a resource for practitioners and researchers working on speech technology across many languages.
A new research paper introduces Orukeet, a speech recognition model that replaces half of an adapted Parakeet encoder's temporal filtering layers with 12,288 fitted Gabor kernels, which are then kept frozen. The remaining parameters are trained on multilingual and multi-accent speech data, followed by a final adaptation and checkpoint selection stage. The approach aims to extend strong transcription models to more languages and accents.
A new arXiv study investigates self-supervised speech foundation models, which are widely deployed for speech recognition and to supply tokens for speech-capable language models. The authors analyze what these models' internal representations encode, testing whether they capture genuine word-level linguistic structure rather than only acoustic patterns. The results bear on how such models should be interpreted and used in downstream speech applications.
Researchers have released BaltiVoice, a 16.8-hour read-speech corpus with 10,060 validated utterances for Balti, a Tibetic language spoken in Gilgit-Baltistan, Pakistan. The language previously had no publicly available speech recognition resources, making this the first open dataset and ASR model for Balti. The team fine-tuned OpenAI's Whisper architecture on the corpus to enable automatic speech recognition for the language.