Improving Speaker Discrimination of Target Speech Extraction With Time-Domain Speakerbeam

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU136480" target="_blank" >RIV/00216305:26230/20:PU136480 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9054683" target="_blank" >https://ieeexplore.ieee.org/document/9054683</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9054683" target="_blank" >10.1109/ICASSP40776.2020.9054683</a>

Result language

angličtina

Original language name

Improving Speaker Discrimination of Target Speech Extraction With Time-Domain Speakerbeam

Original language description

Target speech extraction, which extracts a single target source in a mixture given clues about the target speaker, has attracted increasing attention. We have recently proposed SpeakerBeam, which exploits an adaptation utterance of the target speaker to extract his/her voice characteristics that are then used to guide a neural network towards extracting speech of that speaker. SpeakerBeam presents a practical alternative to speech separation as it enables tracking speech of a target speaker across utterances, and achieves promising speech extraction performance. However, it sometimes fails when speakers have similar voice characteristics, such as in same-gender mixtures, because it is difficult to discriminate the target speaker from the interfering speakers. In this paper, we investigate strategies for improving the speaker discrimination capability of SpeakerBeam. First, we propose a time-domain implementation of SpeakerBeam similar to that proposed for a time-domain audio separation network (TasNet), which has achieved state-of-the-art performance for speech separation. Besides, we investigate (1) the use of spatial features to better discriminate speakers when microphone array recordings are available, (2) adding an auxiliary speaker identification loss for helping to learn more discriminative voice characteristics. We show experimentally that these strategies greatly improve speech extraction performance, especially for same-gender mixtures, and outperform TasNet in terms of target speech extraction.

Czech name

—

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/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISBN

978-1-5090-6631-5

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

691-695

Publisher name

IEEE Signal Processing Society

Place of publication

Barcelona

Event location

Barcelona

Event date

May 4, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000615970400138