asr

This paper investigates cross-lingual transfer for low-resource ASR in Warlpiri, proposing a similarity-based framework combining acoustic and linguistic features to select optimal source languages. Experiments show that acoustically similar languages like Assamese and Hindi significantly reduce word and character error rates.

This paper proposes using Group Relative Policy Optimization (GRPO) for adapting LLM-based ASR models to regulated domains using only synthetic speech, achieving 40-45% relative WER reduction over supervised fine-tuning.

This paper introduces a gradient-based speech-to-text alignment method applicable to any differentiable ASR model, including CTC, transducer, attention-based encoder-decoder, and speech large language models, requiring no training or model modification.

MOSS-Transcribe-Diarize-0.9B is an open-source end-to-end audio understanding model for long-form multi-speaker transcription, diarization, and timestamp generation, released by Mosi AI under Apache 2.0.

audio.cpp update introduces streaming support and four ASR/STT models (Nemotron 3.5, Higgs Audio STT, VibeVoice ASR, Hviske ASR) in native C++/GGML, achieving up to 2.41x faster than Python with competitive WER and VRAM usage.

This research investigates cross-lingual transfer learning from Sinhala to Dhivehi for automatic speech recognition, achieving significant improvements in word error rate compared to Dhivehi-only baselines.

This paper revisits the classic relation between language model perplexity and ASR word error rate in the context of modern end-to-end ASR systems, finding that while external LMs still improve WER, the log-log linear relation still holds but is affected by internal language modeling in encoder-decoder models.

S-DiverSe is a 3.2-hour corpus of Spanish speech from 22 speakers with neurological conditions (ALS, Parkinson's, stroke), designed to support ASR evaluation for pathological speech. Baseline experiments show heuristic post-processing outperforms fine-tuning for this domain.

Introduces a reinforcement learning with verifiable rewards recipe for data-efficient adaptation of audio-language models to code-switched ASR, achieving significant gains across 10 language pairs with minimal data.

This paper describes NAVER LABS Europe's submission to the IWSLT 2026 instruction-following short track, improving upon their previous winning system by using a new speech projector (SpeechMapper) trained solely on ASR data and augmenting training with a synthetic SQA dataset (fakACL). The resulting system ties for first place in the constrained track while using a weaker LLM backbone.

This paper proposes Joint Speech-Text Interleaved Pretraining (JSTIP), a pretraining strategy that constructs word-level and segment-level interleaved speech-text sequences to improve ASR entity accuracy and reduce the modality gap between speech and text, showing competitive performance on domain adaptation and zero-shot speech question answering.

This paper presents a personalized ASR system for a dysarthric speaker by fine-tuning the Whisper foundation model, achieving 9.7% word error rate using only 22.5 hours of adaptation data plus 8.8 hours of user corrections. The results demonstrate that personalized fine-tuning can make foundation ASR models substantially more effective for dysarthric speech, with a deployed mobile application enabling real-world data collection.

This paper proposes a tone-conditioned curriculum learning framework for low-resource Bantu speech recognition, combining hybrid difficulty scoring, gated adapters, and staged curriculum training. Evaluations on six Southern Bantu languages show that W2V-BERT outperforms Whisper on Nguni languages while Whisper performs better on Sotho-Tswana languages.

This paper proposes a dual-reference benchmarking approach for atypical ASR, using both verbatim and intended transcriptions to evaluate 11 ASR models on stuttered speech, highlighting the importance of selecting the appropriate reference depending on the use case.

This paper investigates the distributional gap between synthetic and real speech in LLM-based ASR systems, identifies where the LLM separates them, and proposes using layer-selection and RIR augmentation to match real-data baselines with less real data.

Whisperian is an Android application that enables users to use a microphone with local automatic speech recognition (ASR) models, and it is available on the Play Store.

This paper audits multilingual clinical ASR systems on psychiatric interviews in Indian languages and proposes SamaVaani, a unified debiasing technique to improve performance and fairness across demographic groups.

This paper describes FBK's submission to the IWSLT 2026 Instruction Following shared task, developing SpeechLLMs for short-form and long-form speech instruction following, exploring segmentation methods and achieving robust long-form performance with fixed 30-second segmentation.

Proposes G-SPIN, a lightweight framework that combines phonetic graph modeling with contextual language understanding for correcting ASR errors, using a GNN to generate phonetically plausible candidate tokens, an MLM for local scoring, and an LLM for final re-ranking, all operating at inference time.

Introduces the FFASR Leaderboard, an open, community-driven benchmark for evaluating automatic speech recognition models under realistic far-field acoustic conditions, highlighting the significant performance gap between near-field and far-field scenarios.