The paper introduces a linguistic-aware agentic data sampling framework for multilingual automatic speech recognition (ASR). It embeds a data sampling LLM agent into the training loop, guided by a rubric that analyzes ASR errors across phonological, morphological, and syntactic dimensions. Experiments on twelve Common Voice languages with Whisper-medium demonstrate that the method outperforms full-data training while using less data, fewer computational resources, and achieving faster convergence.
Multilingual automatic speech recognition (ASR) has achieved rapid progress with large-scale pretrained models and multilingual speech corpora. Previous studies have proved the pivotal role of data sa...
The paper introduces a unified phoneme‑based TTS‑to‑ASR augmentation pipeline that uses a multilingual TTS model with language‑ID conditioning and incorporates grapheme‑to‑phoneme conversion, reference‑speech filtering, and candidate‑text selection. It proposes phoneme‑frequency‑guided selection (PFGS) to rank sentences based on phoneme frequencies from real ASR labels, and demonstrates that random augmentation and PFGS both improve ASR performance across Arabic, French, Italian, and Portuguese test sets, with PFGS yielding up to a 19.3% relative WER reduction. The study also shows that filtering reference speech can further lower WER by up to 0.59 points on certain datasets.
arXiv:2609.09554v1 Announce Type: new Abstract: We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. L...
arXiv:2610.19388v1 Announce Type: new Abstract: Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresen...
The paper introduces a phoneme-guided text-to-speech (TTS) augmentation pipeline for automatic speech recognition (ASR) that links multilingual speech generation with candidate-text selection and reference-speech quality control. It proposes phoneme-frequency-guided selection (PFGS), which prioritizes candidate texts containing common phonetic content based on real ASR training transcripts. Experiments across four languages and 13 test sets show that random text selection improves recognition on 11 test sets, while PFGS further improves nine test sets with relative word error rate reductions up to 19.3%, and reference-speech filtering also contributes to performance gains.
arXiv:2603. 29042v2 Announce Type: replace-cross Abstract: Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive.
We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. Large end-to-end Transformer-based ASR models such...
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
arXiv:2609.15758v1 Announce Type: new Abstract: Extending large-scale multilingual automatic speech recognition (ASR) models to low-resource languages remains challenging. Model performance is skewed...
arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.
arXiv:2610.19869v1 Announce Type: cross Abstract: We introduce SEA-HELM Audio, a Southeast Asian (SEA) multilingual evaluation framework that extends the text-based SEA-HELM benchmark to assess perfo...
arXiv:2610.08063v1 Announce Type: cross Abstract: This paper presents the HINTT system submitted to the 2nd Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM). We a...