2 citations · 4 across the 7 of their papers we have counts for
9 papers
SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation
Ruohan Liu, Shukang Yin, Tao Wang +6
Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and…
OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
Han Zhu, Lingxuan Ye, Wei Kang +7
We present OmniVoice, a massively multilingual zero-shot text-to-speech (TTS) model that scales to over 600 languages. At its core is a novel diffusion language model-style discret…
MiMo-V2-Flash Technical Report
Core Team, Bangjun Xiao, Bingquan Xia +123
We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-…
MiMo-Audio: Audio Language Models are Few-Shot Learners
Core Team, Dong Zhang, Gang Wang +97
Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with…
Flow2GAN: Hybrid Flow Matching and GAN with Multi-Resolution Network for Few-step High-Fidelity Audio Generation
Zengwei Yao, Wei Kang, Han Zhu +8
Existing dominant methods for audio generation include Generative Adversarial Networks (GANs) and diffusion-based methods like Flow Matching. GANs suffer from slow convergence duri…
ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching
Han Zhu, Wei Kang, Liyong Guo +11
Generating spoken dialogue is inherently more complex than monologue text-to-speech (TTS), as it demands both realistic turn-taking and the maintenance of distinct speaker timbres.…