Přístupnostní navigace
E-application
Search Search Close
Publication detail
DELCROIX, M. ŽMOLÍKOVÁ, K. KINOSHITA, K. ARAKI, S. OGAWA, A. NAKATANI, T.
Original Title
SpeakerBeam: A New Deep Learning Technology for Extracting Speech of a Target Speaker Based on the Speaker's Voice Characteristics
Type
journal article in Scopus
Language
English
Original Abstract
In a noisy environment such as a cocktail party, humans can focus on listening to a desired speaker, an ability known as selective hearing. Current approaches developed to realize computational selective hearing require knowing the position of the target speaker, which limits their practical usage. This article introduces SpeakerBeam, a deep learning based approach for computational selective hearing based on the characteristics of the target speakers voice. SpeakerBeam requires only a small amount of speech data from the target speaker to compute his/her voice characteristics. It can then extract the speech of that speaker regardless of his/her position or the number of speakers talking in the background.
Keywords
deep learning, target speaker extraction, SpeakerBeam
Authors
DELCROIX, M.; ŽMOLÍKOVÁ, K.; KINOSHITA, K.; ARAKI, S.; OGAWA, A.; NAKATANI, T.
Released
1. 11. 2018
ISBN
1348-3447
Periodical
NTT Technical Review
Year of study
16
Number
11
State
Japan
Pages from
19
Pages to
24
Pages count
6
URL
https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201811all.pdf&mode=show_pdf
BibTex
@article{BUT185149, author="DELCROIX, M. and ŽMOLÍKOVÁ, K. and KINOSHITA, K. and ARAKI, S. and OGAWA, A. and NAKATANI, T.", title="SpeakerBeam: A New Deep Learning Technology for Extracting Speech of a Target Speaker Based on the Speaker's Voice Characteristics", journal="NTT Technical Review", year="2018", volume="16", number="11", pages="19--24", issn="1348-3447", url="https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201811all.pdf&mode=show_pdf" }