activity
20192026
most citedNeural Target Speech Extraction: An Overview

120 citations · 349 across the 85 of their papers we have counts for

collaborators
Showing 2020 · eess.ASShow all

12 papers · 2 filters

eess.AS2020★ 6 cited

Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording

Cong Han, Yi Luo, Chenda Li +8

Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years. Recent research includes extracting target speech by us…

eess.AS2020

Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation

Katerina Zmolikova, Marc Delcroix, Lukáš Burget +2

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integr…

eess.AS2020

Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds

Keisuke Kinoshita, Marc Delcroix, Naohiro Tawara

Recent diarization technologies can be categorized into two approaches, i.e., clustering and end-to-end neural approaches, which have different pros and cons. The clustering-based…

eess.AS2020

Far-Field Automatic Speech Recognition

Reinhold Haeb-Umbach, Jahn Heymann, Lukas Drude +3

The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention…

eess.AS2020★ 3 cited

Multi-path RNN for hierarchical modeling of long sequential data and its application to speaker stream separation

Keisuke Kinoshita, Thilo von Neumann, Marc Delcroix +2

Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but e…

eess.AS2020★ 4 cited

Listen to What You Want: Neural Network-based Universal Sound Selector

Tsubasa Ochiai, Marc Delcroix, Yuma Koizumi +3

Being able to control the acoustic events (AEs) to which we want to listen would allow the development of more controllable hearable devices. This paper addresses the AE sound sele…