#asr

9 curated events

papersTODAY 04:00 UTC

arXiv paper proposes sequential adapter stacking for low-resource ASR

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.

asr/low-resource/cross-lingual/adapters

papersTODAY 04:00 UTC

Controllable Dysarthric Speech Synthesis for Speaker-Diverse ASR Training

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.

speech-synthesis/asr/accessibility/research

papersTODAY 04:00 UTC

Typhoon ASR Streaming enables low-latency Thai speech recognition

A new arXiv paper introduces Typhoon ASR Streaming, a deployable Thai speech recognition system designed for low-latency use cases such as live captioning and voice agents. Most open Thai ASR models are offline and Whisper-based, which prevents them from transcribing incrementally. The system uses real-time shallow fusion and remains steerable during streaming.

speech-recognition/thai/low-latency/asr

papersTODAY 04:00 UTC

Replay-Based Editing Reduces Timestamp Drift in Autoregressive ASR

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.

asr/timestamps/continual-learning/speech

papersSEP 10 04:00 UTC

Study examines phoneme-based TTS augmentation pipeline for improving ASR training

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.

asr/tts/speech/research

papersSEP 10 04:00 UTC

Orukeet paper proposes multilingual ASR using frozen Gabor kernels in Parakeet encoder

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.

asr/speech-recognition/multilingual

modelsSEP 10 04:00 UTC

Qwen-Audio-3.0-ASR technical report details LLM-integrated speech recognition

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.

speech-recognition/asr/llm/qwen

papersSEP 10 04:00 UTC

BuzzASR: 100+ monolingual fine-tuned Whisper models for speech recognition in 102 languages

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.

speech-recognition/whisper/multilingual/asr

papersSEP 10 04:00 UTC

Researchers propose fine-grained error correction to improve Korean ASR for consultation services

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.

asr/korean/error-correction/customer-service