8 citations · 24 across the 34 of their papers we have counts for
38 papers
XTurnix: Large-Scale Self-Supervised Turn Control through Two-State Binary Decisions
Zhanxun Liu, Yifan Duan, Hengtao Wu +7
General turn-taking behavior in real-time dialogue systems requires deciding whether to keep listening or start responding while listening, and whether to continue or stop while sp…
MOSS-VL Technical Report
Pengyu Wang, Chenkun Tan, Shaojun Zhou +29
We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across th…
OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation
Jun Zhan, Chen Yang, Yitian Gong +23
Recent generative models are moving beyond silent video or standalone audio synthesis toward the joint generation of synchronized audio and video. Despite this progress, jointly ge…
MOSS-Audio Technical Report
Chen Yang, Chufan Yu, Hanfu Chen +27
MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped trans…
X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning
Rixi Xu, Qingyu Liu, Haitao Li +10
In this paper, we present X-Voice, a 0.4B multilingual zero-shot voice cloning model that clones arbitrary voices and enables everyone to speak 30 languages. X-Voice is trained on…
MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions
Kexin Huang, Liwei Fan, Botian Jiang +11
Voice design from natural language aims to generate speaker timbres directly from free-form textual descriptions, allowing users to create voices tailored to specific roles, person…
MOSS-TTSD: Text to Spoken Dialogue Generation
Yuqian Zhang, Donghua Yu, Zhengyuan Lin +15
Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but poses significant challenges compared to single-utterance t…
MOSS-TTS Technical Report
Yitian Gong, Botian Jiang, Yiwei Zhao +23
This technical report presents MOSS-TTS, a speech generation foundation model built on a scalable recipe: discrete audio tokens, autoregressive modeling, and large-scale pretrainin…
AI Can Learn Scientific Taste
Jingqi Tong, Mingzhe Li, Hangcheng Li +20
Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with the potential for long-term sci…
MOVA: Towards Scalable and Synchronized Video-Audio Generation
OpenMOSS Team, Donghua Yu, Mingshu Chen +38
Audio is indispensable for real-world video, yet generation models have largely overlooked audio components. Current approaches to producing audio-visual content often rely on casc…
WESR: Scaling and Evaluating Word-level Event-Speech Recognition
Chenchen Yang, Kexin Huang, Liwei Fan +8
Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying. While semantic transcription is well-studied, the precise localiza…
MOSS Transcribe Diarize Technical Report
MOSI. AI, :, Donghua Yu +23
Speaker-Attributed, Time-Stamped Transcription (SATS) aims to transcribe what is said and to precisely determine the timing of each speaker, which is particularly valuable for meet…
MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance
Xingjian Zhao, Zhe Xu, Qinyuan Cheng +20
Spoken dialogue systems often rely on cascaded pipelines that transcribe, process, and resynthesize speech. While effective, this design discards paralinguistic cues and limits exp…
VStyle: A Benchmark for Voice Style Adaptation with Spoken Instructions
Jun Zhan, Mingyang Han, Yuxuan Xie +11
Spoken language models (SLMs) have emerged as a unified paradigm for speech understanding and generation, enabling natural human machine interaction. However, while most progress h…
CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation
Ruifan Deng, Yitian Gong, Qinghui Gao +5
With the rise of multimodal large language models (LLMs), audio codec plays an increasingly vital role in encoding audio into discrete tokens, enabling integration of audio into te…
Dynamic and Generalizable Process Reward Modeling
Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng +3
Process Reward Models (PRMs) are crucial for guiding Large Language Models (LLMs) in complex scenarios by providing dense reward signals. However, existing PRMs primarily rely on h…
XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs
Yitian Gong, Luozhijie Jin, Ruifan Deng +6
Speech codecs serve as bridges between speech signals and large language models. An ideal codec for speech language models should not only preserve acoustic information but also ca…
Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections
Bo Wang, Qinyuan Cheng, Runyu Peng +7
Post-training processes are essential phases in grounding pre-trained language models to real-world tasks, with learning from demonstrations or preference signals playing a crucial…
InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems
Kexin Huang, Qian Tu, Liwei Fan +6
In modern speech synthesis, paralinguistic information--such as a speaker's vocal timbre, emotional state, and dynamic prosody--plays a critical role in conveying nuance beyond mer…
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
Yuan Li, Qi Luo, Xiaonan Li +7
Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and mitigate hallucination. However, dense retri…
VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search
Yikun Wang, Siyin Wang, Qinyuan Cheng +5
Recent advancements in Large Vision-Language Models have showcased remarkable capabilities. However, they often falter when confronted with complex reasoning tasks that humans typi…
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
Siyin Wang, Zhaoye Fei, Qinyuan Cheng +4
Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and…
How to Mitigate Overfitting in Weak-to-strong Generalization?
Junhao Shi, Qinyuan Cheng, Zhaoye Fei +3
Aligning powerful AI models on tasks that surpass human evaluation capabilities is the central problem of \textbf{superalignment}. To address this problem, weak-to-strong generaliz…
Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?
Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin +2
The advent of test-time scaling in large language models (LLMs), exemplified by OpenAI's o1 series, has advanced reasoning capabilities by scaling computational resource allocation…