arXiv Computation and Language
Sep 4

Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition

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
arXiv AI
3d ago

Uni-Stream: A Unified Streaming Speech Language Model for Speech Synthesis and Recognition

Uni-Stream is a unified streaming speech language model that supports both speech recognition and synthesis within a single architecture. Built on compact pretrained speech models, it processes interleaved text and speech in real time and achieves performance comparable to task‑specific models through a two‑stage training approach. The authors also introduce a data annotation pipeline for streaming training and plan to release the generated transcriptions and word‑level timestamps, while analyzing how speech and text representations occupy distinct latent spaces in the model.

By Xuan Shi, Chang Zeng, Tiantian Feng, Ye Zhang, Jun Liu, Shih-Heng Wang, Lie Lu, Shrikanth Narayanan
arXiv Computation and Language
Sep 11

Streaming Translation and Transcription Through Speech-to-Text Causal Alignment

The paper introduces Hikari, a policy‑free, end‑to‑end model that performs simultaneous speech‑to‑text translation and streaming transcription. It employs a Decoder Time Dilation mechanism to mitigate overuse of WAIT tokens during training and a supervised fine‑tuning strategy that helps the model recover from delays, improving the quality‑latency trade‑off. Despite its modest size, Hikari achieves competitive translation quality at consistently low latency, outperforming larger published IWSLT 2026 submissions and proprietary API systems on en‑ja, en‑de, and en‑ru tasks.

By Roman Koshkin, Jeon Haesung, Lianbo Liu, Hao Shi, Mengjie Zhao, Yusuke Fujita, Yui Sudo