The article reviews a recent Kurdish speech resource release that includes three voices, 35 hours of recorded speech, and a supporting paper. It finds several issues with the public files—mislisted equipment, unlabeled test data, and a coding error—while noting that the download page overstates the results and recommends a single voice for general use. The authors argue that these problems can largely be corrected with existing information, improving the resource’s usability for future work.
By Hiwa Asadpour
arXiv:2607. 08256v1 Announce Type: cross Abstract: Best-of-$N$ (BoN) inference improves content consistency in zero-shot text-to-speech by selecting from $N$ candidates with an automatic speech recognition (ASR) verifier.
By Taehyung Yu, Seongjae Kang
arXiv:2608.22316v1 Announce Type: cross
Abstract: Whether an intermediate stage of modern Arabic handwriting helps or hurts historical Arabic HTR is usually decided from one implementation and one co...
By Sumaih Almarshad, Maram Alamri, Dona Aloraini, Fares Altuwaim, AlJawharh AlOtaibi, Reem Alyabis, Rayah Aldawsari
arXiv:2606. 18717v1 Announce Type: cross Abstract: Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and -- in the case of WordPiece and rule-based analyzers -- failing to decode their output back to the original text.
By Tolga \c{S}akar
The study evaluates seven parameter‑efficient fine‑tuning (PEFT) methods—LoRA, QLoRA, AdaLoRA, DoRA, LoHA, VeRA, and VB‑LoRA—on two ASR back‑ends (Whisper‑large‑v3 and Qwen3‑ASR‑1.7B) for a single post‑stroke Hungarian male speaker with severe dysarthria. Attention‑projection adapters consistently lower character error rates (CER) on both models, with LoRA emerging as the simplest and most effective choice; QLoRA performs worse and offers no memory advantage at this scale. Full fine‑tuning yields the lowest CER, but a 115 MB LoRA that also adapts feed‑forward blocks achieves comparable accuracy with only 3.7 % of the per‑patient storage, and a 5‑minute enrollment audio captures nearly half of the zero‑shot‑to‑30‑minute CER improvement.
whyItMatters":"The paper demonstrates that lightweight PEFT adapters can substantially improve dysarthric ASR performance while keeping storage and computational costs low, offering a practical path for personalized speech recognition in clinical settings."
By Bernard Muller, L\'aszl\'o T\'oth, LaVonne Roberts
The paper shows that HuggingFace’s ByteLevel pre‑tokenizer, which treats a word as a sequence of Unicode letters, splits abugida scripts at every vowel sign, creating a training‑free lower bound on tokenizer fertility. Across 26 languages, all 17 abugidas exhibit increased token counts (up to 9×), while Latin, Cyrillic, Hangul, and Han remain unchanged. The authors demonstrate that correcting the character class reduces Nepali token counts, improves model performance, and that this issue is widespread in popular HuggingFace models.
By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun