arXiv:2608.29170v1 Announce Type: new
Abstract: This paper proposes the Sinitic Romanization Ecosystem, a cross-lingual Sinitic romanization design framework with supporting digital infrastructure an...
By Zijie Zhang, Tan Lee, Yong Cao, Benyou Wang
The paper introduces Tha, a Khmer text normalization and inverse text normalization toolkit that uses weighted finite-state transducers. Tha processes entire lines in a single shortest-path search and employs a second transducer to prevent token boundaries within Khmer syllables. On Google's Khmer test suite, Tha achieves perfect agreement on 274 cardinals and correctly rewrites 153 of 158 real TTS prompts.
By Seanghay Yath
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.
By Maneesha Rani Saha, Catherine Bao, Neal Patwari
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.
By Shikhar Bharadwaj, Chin-Jou Li, Kwanghee Choi, Eunjung Yeo, William Chen, Shinji Watanabe, David R. Mortensen
arXiv:2609. 21362v1 Announce Type: new Abstract: Conventional tokenizers represent text as characters or statistically derived subwords, overlooking the internal phonological structure of syllables and often requiring large vocabularies.
By Nghia Hieu Nguyen, Thai Bao Huynh, Binh-An Dinh-Le, Phu Gia Hoang, Dat Tien Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
The paper introduces a new evaluation framework for phonetic encoding algorithms, using a generalized Rand Index called the Hüllermeier‑Rifqi Index. It measures discordance by comparing pairwise similarity scores of ground‑truth IPA transcriptions with those of encoded strings, adjusted against a random generator. The method is applied to multilingual datasets, assessing recall via collision rate and demonstrating its use in evaluating orthographic transparency.
By Can \"Ozbey, Emre Kaplan, Berkin Deniz Kahya