The paper proposes an embedding‑based subset selection method for fine‑tuning Automatic Speech Recognition models, specifically using Whisper’s pretrained encoder to map audio samples into a fixed‑dimensional space and applying a greedy k‑center algorithm to pick diverse, representative subsets. Experiments on Hebrew and Danish show that k‑center selection outperforms random sampling, especially when the training set is small, and that the chosen subsets exhibit greater lexical diversity. The results suggest that this practical strategy can improve ASR performance under limited data budgets.
Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown strong Automatic Sp...
arXiv:2609.38887v1 Announce Type: cross Abstract: Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effect...
arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-R...
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...
Group Relative Policy Optimisation (GRPO) is applied to a non‑LLM encoder‑decoder ASR model, Whisper‑large‑v3, to improve multilingual speech recognition across 51 African languages. The method mitigates exposure bias from teacher forcing, especially benefiting low‑resource and noisy audio scenarios. The resulting model, SunflowerASR, sets new macro‑averaged WER records on AfriVox‑v2 (0.227) and SimbaBench (0.372).
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...
arXiv:2609.14231v1 Announce Type: cross Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their aco...
arXiv:2509.10452v3 Announce Type: replace-cross Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance....
arXiv:2608.30927v1 Announce Type: cross Abstract: Whisper exposes speech through a fixed 1500-token encoder interface, now a default representation for ASR decoders and Whisper-based speech language...
The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models. By comparing various fine‑tuning strategies—zero‑shot decoding, English‑only, German‑only, two‑stage EN→EN+DE, and direct EN+DE fine‑tuning—it shows that fine‑tuning significantly improves performance, with the best model varying by setting. Layer‑wise encoder analysis reveals that English medical fine‑tuning drives the main representation shift, while multilingual continuation largely preserves the adapted space, and that domain and language signals remain recoverable across layers.
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.
The paper compares encoder‑based and generative decoder‑based large language models for evaluating automatic speech recognition (ASR). It examines BERTScore and SemDist across various LLMs, layers, and pooling strategies, finding that both metrics can strongly correlate with human judgments when properly configured. For generative LLMs, the study explores pairwise hypothesis selection via prompting and direct error classification, showing that while encoder‑based metrics remain competitive, generative models excel in hypothesis comparison and enhance interpretability of ASR evaluation.