Compact Network for Speakerbeam Target Speaker Extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134186" target="_blank" >RIV/00216305:26230/19:PU134186 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8683087" target="_blank" >https://ieeexplore.ieee.org/document/8683087</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP.2019.8683087" target="_blank" >10.1109/ICASSP.2019.8683087</a>

Result language

angličtina

Original language name

Compact Network for Speakerbeam Target Speaker Extraction

Original language description

Speech separation that separates a mixture of speech signals into each of its sources has been an active research topic for a long time and has seen recent progress with the advent of deep learning. A related problem is target speaker extraction, i.e. extraction of only speech of a target speaker out of a mixture, given characteristics of his/her voice. We have recently proposed SpeakerBeam, which is a neural network-based target speaker extraction method. Speaker- Beam uses a speech extraction network that is adapted to the target speaker using auxiliary features derived from an adaptation utterance of that speaker. Initially, we implemented SpeakerBeam with a factorized adaptation layer, which consists of several parallel linear transformations weighted by weights derived from the auxiliary features. The factorized layer is effective for target speech extraction, but it requires a large number of parameters. In this paper, we propose to simply scale the activations of a hidden layer of the speech extraction network with weights derived from the auxiliary features. This simpler approach greatly reduces the number of model parameters by up to 60%, making it much more practical, while maintaining a similar level of performance. We tested our approach on simulated and real noisy and reverberant mixtures, showing the potential of SpeakerBeam for real-life applications. Moreover, we showed that speech extraction performance of SpeakerBeam compares favorably with that of a state-of-the-art speech separation method with a similar network configuration.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/TJ01000208" target="_blank" >TJ01000208: Neural networks for speech signal processing and data mining</a><br>

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

Article name in the collection

Proceedings of ICASSP

ISBN

978-1-5386-4658-8

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

6965-6969

Publisher name

IEEE Signal Processing Society

Place of publication

Brighton

Event location

Brighton

Event date

May 12, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000482554007040