28 citations · 81 across the 9 of their papers we have counts for
9 papers
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Rongjie Huang, Max W. Y. Lam, Jun Wang +4
Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinde…
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Max W. Y. Lam, Jun Wang, Dan Su +1
Diffusion probabilistic models (DPMs) and their extensions have emerged as competitive generative models yet confront challenges of efficient sampling. We propose a new bilateral d…
Bilateral Denoising Diffusion Models
Max W. Y. Lam, Jun Wang, Rongjie Huang +2
Denoising diffusion probabilistic models (DDPMs) have emerged as competitive generative models yet brought challenges to efficient sampling. In this paper, we propose novel bilater…
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition
Max W. Y. Lam, Jun Wang, Chao Weng +2
End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization. To extract learnable…
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation
Max W. Y. Lam, Jun Wang, Dan Su +1
One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throu…
Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect
Jun Wang, Max W. Y. Lam, Dan Su +1
We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. It firstly learns…