The paper introduces a unified model that jointly performs Khmer text recognition and word segmentation, eliminating the need for a separate segmentation step. Using a connectionist-temporal-classification decoder, the model can be instructed to output Khmer text with or without word boundaries. Experiments across document, scene, and handwritten image datasets demonstrate that the model accurately recognizes characters and locates word boundaries, reducing error and latency compared to traditional sequential pipelines.
By Marry Kong, Rina Buoy, Sovisal Chenda, Nguonly Taing, Masakazu Iwamura, Koichi Kise
arXiv:2609.21084v1 Announce Type: cross
Abstract: Modern automatic speech recognition (ASR) systems trained on extremely large datasets can produce transcripts with numbers written in Arabic numerals...
By Stanis{\l}aw Kacprzak, Mieszko Fra\'s
arXiv:2609.14542v1 Announce Type: new
Abstract: Neyshekar is presented as an open Persian read-speech corpus designed for coverage of both formal and informal language, named entities, and longer utt...
By Ahmad Amirivojdan, Farzad Nadiri, Abolfazl Alizadeh, Shaghayegh Yaraghi
arXiv:2608.28635v1 Announce Type: cross
Abstract: Recent multimodal large language models (MLLMs) have advanced document understanding, visual question answering, and text extraction. However, their...
By Nimol Thuon, Panhapin Theang
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.
By Zijie Zhang, Tan Lee
Dual-Form ASR (DF-ASR) is a framework that unifies spoken-form ASR and semantics-aware written-form inverse text normalization (ITN) for Chinese speech recognition. It uses paired spoken- and written-form supervision generated and judged by a large language model, and introduces an ITN-MWER objective to penalize errors on normalization-sensitive spans. DF-ASR also employs a REQUIRE-ITN/FORBID-ITN protocol to separately evaluate required normalization and forbidden-span preservation, achieving superior performance over open-source ASR-ITN systems while maintaining prompt-level control between transcript forms.
By Fengrun Zhang, Li Fu, Wangjin Zhou, Lu Fan, Youzheng Wu, Xiaodong He