19 citations · 59 across the 14 of their papers we have counts for
8 papers · 1 filter
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing
Yen-Ju Lu, Jing Liu, Thomas Thebaud +4
We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to s…
SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer
Helin Wang, Jiarui Hai, Yen-Ju Lu +3
In this paper, we introduce SoloAudio, a novel diffusion-based generative model for target sound extraction (TSE). Our approach trains latent diffusion models on audio, replacing t…
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
Yen-Ju Lu, Xuankai Chang, Chenda Li +10
This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features hav…
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
Yen-Ju Lu, Samuele Cornell, Xuankai Chang +5
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neur…
Conditional Diffusion Probabilistic Model for Speech Enhancement
Yen-Ju Lu, Zhong-Qiu Wang, Shinji Watanabe +3
Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs. While generative models…
A Study on Speech Enhancement Based on Diffusion Probabilistic Model
Yen-Ju Lu, Yu Tsao, Shinji Watanabe
Diffusion probabilistic models have demonstrated an outstanding capability to model natural images and raw audio waveforms through a paired diffusion and reverse processes. The uni…
Incorporating Broad Phonetic Information for Speech Enhancement
Yen-Ju Lu, Chien-Feng Liao, Xugang Lu +2
In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals. Previous studies ha…
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh +9
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our st…