Researchers have published Neyshekar, an openly available Persian read-speech corpus intended to cover formal and informal speech, named entities, and longer sentences. Version 6 contains 62,279 validated recordings totaling 99.02 hours, contributed by 190 speakers. The dataset is aimed at supporting automatic speech recognition work in Persian.
A new arXiv paper examines how language model priors can be leveraged to craft acoustic adversarial attacks against automatic speech recognition systems. It focuses on real-time ASR, where transcription decisions must be made under strict temporal limits using incomplete audio input. The work suggests that this causal constraint creates an exploitable vulnerability in streaming recognition pipelines.
A new study examines how autoregressive speech recognition systems that output timestamps as decoded tokens can gradually lose alignment during long stretches without speech. The authors propose a replay-based distribution editing approach that corrects this drift while limiting forgetting of previously learned behavior. The work targets timestamped transcription without relying on frame-level aligners or inference-time fixes.
Researchers propose a speech synthesis method that separates speaker identity from dysarthric articulation patterns, allowing finer control over generated dysarthric speech. The approach conditions synthesis on individual patients, producing varied synthetic speakers to supplement scarce training data for dysarthric speech recognition. This addresses a field bottlenecked by high speaker variability and limited labeled recordings.
A new arXiv preprint in the cs.CL category describes work on combining large language models with automatic speech recognition systems. The paper focuses on knowledge-merging techniques related to established LM fusion approaches such as shallow fusion and density ratio methods. It appears to be a research contribution rather than a product or model release.
A research paper examines how speech and audio LLMs are typically evaluated, namely by testing whether a waveform prompt outperforms an automatic speech recognition transcript. The authors argue that for closed-set, known tasks this setup mixes together two distinct things: whether the model actually used acoustic information and whether it simply needed the task spelled out. They propose auditing generative audio calls as a way to separate those factors.
A new arXiv preprint describes a method that stacks adapters sequentially to help large multilingual speech recognition models handle low-resource languages. The authors note that current systems perform unevenly, favoring high-resource languages and losing accuracy where labeled audio is scarce. The abstract covers the approach at a high level, with results and evaluation details not included in the excerpt.
A new arXiv preprint argues that word error rate alone hides important failures when speech recognition systems and audio language models handle code-switched speech. The authors propose an evaluation method that accounts for language switches, and apply it to English-Yoruba audio, a low-resource pair with diacritics. They find that strong monolingual benchmark scores do not carry over to this setting.
A new arXiv paper introduces a unified pipeline that uses a single phoneme-based text-to-speech model to generate synthetic training data for automatic speech recognition. The work presents a controlled study of how text selection, reference speech, and augmentation scale affect recognition accuracy.
A newly published arXiv paper tackles recognition errors that occur when automatic speech recognition is deployed in customer consultation settings. The authors introduce a fine-grained error correction method aimed at Korean-language call center dialogue, where even advanced ASR systems continue to make mistakes. The work targets customer service automation and large-scale transcription applications.
Researchers introduce StreamAlign, a speech tokenization method that maps audio into tokens aligned with large language model token spaces while operating in a streaming, low-latency manner. Unlike existing text-aligned tokenizers that depend on offline automatic speech recognition, the approach addresses the latency and alignment limitations that offline processing imposes, enabling more efficient use of pretrained LLMs for speech tasks.
A technical report published on arXiv introduces Qwen-Audio-3.0-ASR, an automatic speech recognition system that combines scaled training data, larger model architecture, and integration with large language models. The paper outlines the system's design choices and evaluates its performance, situating it within recent progress in ASR research.
Hugging Face's Open ASR Leaderboard has expanded its coverage to include a language from the Global South for the first time. The addition broadens the benchmark's evaluation of automatic speech recognition systems beyond the predominantly high-resource languages it previously tracked. It reflects a wider push to measure model performance on underrepresented languages.
WHY IT MATTERS ↘Benchmarks drive where engineering effort goes, so extending a widely cited ASR leaderboard to a Global South language gives vendors and researchers a shared target for measuring quality on languages that commercial incentives alone have largely ignored. The caveat is that a single added language still reflects an underrepresentative sample, so teams should treat it as a starting signal for data collection and evaluation rather than evidence of broad multilingual coverage.