arXiv:2605. 19266v2 Announce Type: replace-cross Abstract: Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informal spoken structures that are often unsuitable for downstream writing-oriented applications.
By Wanyi Ning, Yinshang Guo, Haitao Qian, Jiyuan Cheng, Weiyuan Feng, Yufei Zhang
The paper introduces a unified phoneme‑based TTS‑to‑ASR augmentation pipeline that uses a multilingual TTS model with language‑ID conditioning and incorporates grapheme‑to‑phoneme conversion, reference‑speech filtering, and candidate‑text selection. It proposes phoneme‑frequency‑guided selection (PFGS) to rank sentences based on phoneme frequencies from real ASR labels, and demonstrates that random augmentation and PFGS both improve ASR performance across Arabic, French, Italian, and Portuguese test sets, with PFGS yielding up to a 19.3% relative WER reduction. The study also shows that filtering reference speech can further lower WER by up to 0.59 points on certain datasets.
By Zhen Wang, TianRui Wu, RongQi Han, Hao Wu, Wei Liang
The Qwen-Audio-3.0-ASR Technical Report introduces a Mixture-of-Experts large language model-based automatic speech recognition system that addresses real‑world production challenges such as regional dialects, dynamic entities, hotwords, long‑range context, and disfluent speech. Built on the Qwen backbone and trained on tens of millions of hours of speech data, it supports transcription in 30 languages and 16 Chinese dialects, and offers industry‑domain entity recognition, hierarchical hotword customization, single‑pass polishing, and long‑audio contextual modeling. A streaming variant, Qwen-Audio-3.0-ASR-Streaming, is also presented for low‑latency applications, with evaluations showing state‑of‑the‑art performance against leading commercial systems.
By Chuanmeng Bian, Daren Chen, Peixin Chen, Zhigao Chen, Zhiyun Fan, Zhifu Gao, Bo Gong, Qing Gu, Jiajun He, Yawei Hu, Yunjie Ji, Jingbei Li, Xiangang Li, Xu Li, Zengxi Li, Zheng Li, Chengdong Liang, Baiji Liu, Ying Liu, Bin Ma, Yiping Peng, Yuezhang Peng, Zhendong Peng, Yu Pu, Yang Shi, Xin Shu, Jian Tang, Biao Tian, Peiyao Wang, Tianzi Wang, Wen Wang, Wupeng Wang, Cheng Wen, Yuzhong Wu, Zijian Xia, Yunchong Xiao, Nan Yang, Jianwei Yu, Jixing Yu, Binbin Zhang, Lei Zhang, Sitong Zhao, Guangdong Zhou, Yuan Zhou, Jianheng Zhuo
The paper presents a method for creating a compact fixed‑voice Thai text‑to‑speech system by training a student model on synthetic speech generated from a large voice‑cloning teacher. By using only a short 15‑second voice reference and carefully filtering synthetic data, the authors build an 82‑million‑parameter model, Wayu‑Paxa‑TTS‑Edge, that runs on device without reference audio. The system achieves strong performance—68.2 % challenge‑set keyword accuracy, 91.4 % pause precision, and low character error rates—while outperforming its teacher and approaching the quality of a larger Gemini 3.1 model.
By Kunat Pipatanakul, Potsawee Manakul, Warit Sirichotedumrong, Sittipong Sripaisarnmongkol, Pakorn Nathong, Phatrasek Jirabovonvisut