23 citations · 46 across the 17 of their papers we have counts for
25 papers
Diffusion-based Generative Modeling with Discriminative Guidance for Streamable Speech Enhancement
Chenda Li, Samuele Cornell, Shinji Watanabe +1
Diffusion-based generative models (DGMs) have recently attracted attention in speech enhancement research (SE) as previous works showed a remarkable generalization capability. Howe…
AnoPatch: Towards Better Consistency in Machine Anomalous Sound Detection
Anbai Jiang, Bing Han, Zhiqiang Lv +6
Large pre-trained models have demonstrated dominant performances in multiple areas, where the consistency between pre-training and fine-tuning is the key to success. However, few w…
Generating Speakers by Prompting Listener Impressions for Pre-trained Multi-Speaker Text-to-Speech Systems
Zhengyang Chen, Xuechen Liu, Erica Cooper +2
This paper proposes a speech synthesis system that allows users to specify and control the acoustic characteristics of a speaker by means of prompts describing the speaker's traits…
Target Speech Diarization with Multimodal Prompts
Yidi Jiang, Ruijie Tao, Zhengyang Chen +2
Traditional speaker diarization seeks to detect ``who spoke when'' according to speaker characteristics. Extending to target speech diarization, we detect ``when target event occur…
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement
Wangyou Zhang, Robin Scheibler, Kohei Saijo +9
The last decade has witnessed significant advancements in deep learning-based speech enhancement (SE). However, most existing SE research has limitations on the coverage of SE sub-…
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech Enhancement
Wangyou Zhang, Kohei Saijo, Jee-weon Jung +3
Deep learning-based speech enhancement (SE) models have achieved impressive performance in the past decade. Numerous advanced architectures have been designed to deliver state-of-t…