7 citations · 7 across the 11 of their papers we have counts for
13 papers
Optimal Design of Shaped Pupil Lyot Coronagraph for China's Large Segmented Telescope
Kaining Yang, He Sun, Bin B. Ren +7
China's next-generation large telescopes, such as the Expandable Aperture Segmented Telescope (EAST) and the Large Optical/Infrared Telescope (LOT), are being planned as flagship f…
Full-duplex Spoken Language Modeling with Discrete Diffusion
Chen Chen, Pin-Jui Ku, Ankita Pasad +3
We introduce Diffuplex, a full-duplex spoken language model based on discrete diffusion that achieves an unprecedented balance between linguistic intelligence and response efficien…
Large Language Continuous Diffusion Models
Zhihan Yang, Wei Guo, Jean-Marie Lemercier +14
Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and…
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
Jean-Marie Lemercier, Tomas Geffner, Karsten Kreis +3
Diffusion language models intrinsically fail to capture correlations between decoded tokens, which leads to a harsh trade-off between sampling quality and throughput. To solve this…
Discrete Diffusion for Generative Modeling of Text-Aligned Speech Tokens
Pin-Jui Ku, He Huang, Jean-Marie Lemercier +3
This paper introduces a discrete diffusion model (DDM) framework for text-aligned speech tokenization and reconstruction. By replacing the auto-regressive speech decoder with a dis…
NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference
Edresson Casanova, Paarth Neekhara, Ryan Langman +6
Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling…
Universal Speech Enhancement with Regression and Generative Mamba
Rong Chao, Rauf Nasretdinov, Yu-Chiang Frank Wang +3
The Interspeech 2025 URGENT Challenge aimed to advance universal, robust, and generalizable speech enhancement by unifying speech enhancement tasks across a wide variety of conditi…
Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration
Pin-Jui Ku, Alexander H. Liu, Roman Korostik +3
This paper proposes a generative pretraining foundation model for high-quality speech restoration tasks. By directly operating on complex-valued short-time Fourier transform coeffi…
Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference
Edresson Casanova, Ryan Langman, Paarth Neekhara +5
Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelin…
Schrödinger Bridge for Generative Speech Enhancement
Ante Jukić, Roman Korostik, Jagadeesh Balam +1
This paper proposes a generative speech enhancement model based on Schrödinger bridge (SB). The proposed model is employing a tractable SB to formulate a data-to-data process betwe…
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations
Kunal Dhawan, Nithin Rao Koluguri, Ante Jukić +3
Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech rec…
Spectral Codecs: Improving Non-Autoregressive Speech Synthesis with Spectrogram-Based Audio Codecs
Ryan Langman, Ante Jukić, Kunal Dhawan +2
Historically, most speech models in machine-learning have used the mel-spectrogram as a speech representation. Recently, discrete audio tokens produced by neural audio codecs have…