Publication detail

SpeakerBeam: A New Deep Learning Technology for Extracting Speech of a Target Speaker Based on the Speaker's Voice Characteristics

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

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"
}