← Back to all news
arXiv Computation and Language

Simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems

Simulstream is an open‑source toolkit designed to evaluate and demonstrate streaming speech‑to‑text translation systems. It supports both incremental and re‑translation decoding on long‑form speech, offers fine‑grained logging for quality and latency metrics, and includes an interactive web interface for real‑time visualization and comparison. The toolkit addresses the fragmented evaluation landscape by providing a unified framework that accommodates different decoding strategies and input formats.

Related stories

arXiv AI
Sep 4

X-Translator: A Real-Time Multilingual Speaker-Aware Speech-to-Speech Translation System

X-Translator is a low‑cost, modular real‑time speech‑to‑speech translation system that integrates streaming ASR, machine translation, and prompt‑conditioned TTS, managed by a session‑level runtime controller. It uses incremental segment commitment to stabilize ASR streams and an online speaker prompt manager to maintain speaker consistency across multi‑speaker conversations. The system is evaluated on translation quality, speech naturalness, latency, and speaker preservation using OpenSTBench, and its code and demo are publicly available on GitHub.

By Yuxiang Zhao, Yichi Zhang, Yanjie An, Yanqiao Zhu, Zhanxun Liu, Yushen Chen, Qixi Zheng, Haina Zhu, Yunchong Xiao, Keqi Deng, Shuai Fan, Kai Yu, Xie Chen
nlpmultimodal
More like this →
Hugging Face Trending Papers
Aug 3

The Role of Disfluencies in Speech Translation

Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them. We show this comes at a cost: disfluencies carry meaning that gets lost when speech is cleaned up.

benchmarks
More like this →
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
nlpfine-tuningsafety
More like this →
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
llmsmultimodal
More like this →