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The paper introduces an ASCII‑only romanization system that covers all phonemic distinctions in Thai and Lao, including segmental contrasts, vowel length, and lexical tone. It ensures one‑symbol‑one‑phoneme transparency and systematic cross‑lingual correspondence between the two languages, while also aligning with Pinyin and Jyutping where possible. The design prioritizes synchronic phonetic clarity, offers optional historical tone annotations, and results in a readable, keyboard‑friendly, machine‑processable representation useful for language learning and cross‑lingual speech processing.
The study evaluates how different input representations—orthographic text, IPA transcription, and romanization—affect cross‑lingual transfer in autoregressive multilingual language models. Across three model sizes and eight languages grouped into typologically motivated pairs, romanized pretraining consistently outperforms native orthography and IPA, especially as model scale increases. Fine‑tuning a text‑pretrained model on romanized data can harm performance on languages already covered by the base model, suggesting romanization should be integrated at pretraining rather than applied later.
The paper evaluates bias in phoneme-based automatic speech recognition (ASR) systems, focusing on WhisperIPA and ZIPA, which produce International Phonetic Alphabet (IPA) transcriptions. Using multilingual speech corpora and demographically annotated English datasets, the authors compare model-generated IPA against grapheme-to-phoneme (G2P) outputs with both standard phoneme error rate (PER) and a new Soft PER metric that allows linguistically similar substitutions. The study finds persistent disparities across language, gender, accent, ethnicity, and age, even when accounting for acceptable phonemic variation.
arXiv:2607.18317v5 Announce Type: replace-cross Abstract: We present TTSYoruba, a rule-based concatenative diphone speech synthesizer for Yoruba, deployed at online as part of the YorubaName.com open...
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:2609.38627v2 Announce Type: new Abstract: Yor\`ub\'a is a tonal language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexi...