SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134173" target="_blank" >RIV/00216305:26230/19:PU134173 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8736286" target="_blank" >https://ieeexplore.ieee.org/document/8736286</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JSTSP.2019.2922820" target="_blank" >10.1109/JSTSP.2019.2922820</a>

Result language

angličtina

Original language name

SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures

Original language description

The processing of speech corrupted by interfering overlapping speakers is one of the challenging problems with regards to todays automatic speech recognition systems. Recently, approaches based on deep learning have made great progress toward solving this problem. Most of these approaches tackle the problem as speech separation, i.e., they blindly recover all the speakers from the mixture. In some scenarios, such as smart personal devices, we may however be interested in recovering one target speaker froma mixture. In this paper, we introduce Speaker- Beam, a method for extracting a target speaker from the mixture based on an adaptation utterance spoken by the target speaker. Formulating the problem as speaker extraction avoids certain issues such as label permutation and the need to determine the number of speakers in the mixture.With SpeakerBeam, we jointly learn to extract a representation from the adaptation utterance characterizing the target speaker and to use this representation to extract the speaker. We explore several ways to do this, mostly inspired by speaker adaptation in acoustic models for automatic speech recognition. We evaluate the performance on the widely used WSJ0-2mix andWSJ0-3mix datasets, and these datasets modified with more noise or more realistic overlapping patterns. We further analyze the learned behavior by exploring the speaker representations and assessing the effect of the length of the adaptation data. The results show the benefit of including speaker information in the processing and the effectiveness of the proposed method.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE J-STSP

ISSN

1932-4553

e-ISSN

1941-0484

Volume of the periodical

13

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

800-814

UT code for WoS article

000477715300003

EID of the result in the Scopus database

2-s2.0-85069900431