Neural Target Speech Extraction: An overview
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149430" target="_blank" >RIV/00216305:26230/23:PU149430 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10113382" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10113382</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MSP.2023.3240008" target="_blank" >10.1109/MSP.2023.3240008</a>
Alternative languages
Result language
angličtina
Original language name
Neural Target Speech Extraction: An overview
Original language description
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail party effect . For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and nontarget speech signals share similar characteristics, complicating their discrimination. Target speech/speaker extraction (TSE) isolates the speech signal of a target speaker from a mixture of several speakers, with or without noises and reverberations, using clues that identify the speaker in the mixture. Such clues might be a spatial clue indicating the direction of the target speaker, a video of the speaker's lips, and a prerecorded enrollment utterance from which the speaker's voice characteristics can be derived. TSE is an emerging field of research that has received increased attention in recent years because it offers a practical approach to the cocktail party problem and involves such aspects of signal processing as audio, visual, and array processing as well as deep learning. This article focuses on recent neural-based approaches and presents an in-depth overview of TSE. We guide readers through the different major approaches, emphasizing the similarities among frameworks and discussing potential future directions.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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)
Result continuities
Project
<a href="/en/project/LTAIN19087" target="_blank" >LTAIN19087: Multi-linguality in speech technologies</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE SIGNAL PROCESSING MAGAZINE
ISSN
1053-5888
e-ISSN
1558-0792
Volume of the periodical
40
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
8-29
UT code for WoS article
000981974000003
EID of the result in the Scopus database
2-s2.0-85159861514