1 citations · 2 across the 8 of their papers we have counts for
9 papers
Are Deep Speech Denoising Models Robust to Adversarial Noise?
Will Schwarzer, Neel Chaudhari, Philip S. Thomas +2
Deep noise suppression (DNS) models enjoy widespread use throughout a variety of high-stakes speech applications. However, we show that four recent DNS models can each be reduced t…
Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation
Gerard I. Gállego, Roy Fejgin, Chunghsin Yeh +2
Audio token modeling has become a powerful framework for speech synthesis, with two-stage approaches employing semantic tokens remaining prevalent. In this paper, we aim to simplif…
Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility
Xiaoyu Liu, Xu Li, Joan Serrà +1
Speech restoration aims at restoring full-band speech with high quality and intelligibility, considering a diverse set of distortions. MaskSR is a recently proposed generative mode…
MaskSR: Masked Language Model for Full-band Speech Restoration
Xu Li, Qirui Wang, Xiaoyu Liu
Speech restoration aims at restoring high quality speech in the presence of a diverse set of distortions. Although several deep learning paradigms have been studied for this task,…
GASS: Generalizing Audio Source Separation with Large-scale Data
Jordi Pons, Xiaoyu Liu, Santiago Pascual +1
Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the pote…
CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models
Hao-Wen Dong, Xiaoyu Liu, Jordi Pons +5
Recent work has studied text-to-audio synthesis using large amounts of paired text-audio data. However, audio recordings with high-quality text annotations can be difficult to acqu…
Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
Xiaoyu Liu, Xu Li, Joan Serrà
Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep…
On permutation invariant training for speech source separation
Xiaoyu Liu, Jordi Pons
We study permutation invariant training (PIT), which targets at the permutation ambiguity problem for speaker independent source separation models. We extend two state-of-the-art P…
An empirical study of Conv-TasNet
Berkan Kadioglu, Michael Horgan, Xiaoyu Liu +3
Conv-TasNet is a recently proposed waveform-based deep neural network that achieves state-of-the-art performance in speech source separation. Its architecture consists of a learnab…