Target Speech Extraction: Independent Vector Extraction Guided by Supervised Speaker Identification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F22%3A00009853" target="_blank" >RIV/46747885:24220/22:00009853 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9829828" target="_blank" >https://ieeexplore.ieee.org/document/9829828</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TASLP.2022.3190739" target="_blank" >10.1109/TASLP.2022.3190739</a>
Alternative languages
Result language
angličtina
Original language name
Target Speech Extraction: Independent Vector Extraction Guided by Supervised Speaker Identification
Original language description
This manuscript proposes a novel robust procedure for the extraction of a speaker of interest (SOI) from a mixture of audio sources. The estimation of the SOI is performed via independent vector extraction (IVE). Since the blind IVE cannot distinguish the target source by itself, it is guided towards the SOI via frame-wise speaker identification based on deep learning. Still, an incorrect speaker can be extracted due to guidance failings, especially when processing challenging data. To identify such cases, we propose a criterion for non-intrusively assessing the estimated speaker. It utilizes the same model as the speaker identification, so no additional training is required. When incorrect extraction is detected, we propose a ``deflation‘‘ step in which the incorrect source is subtracted from the mixture and, subsequently, another attempt to extract the SOI is performed. The process is repeated until successful extraction is achieved. The proposed procedure is experimentally tested on artificial and real-world datasets containing challenging phenomena: source movements, reverberation, transient noise, or microphone failures. The method is compared with state-of-the-art blind algorithms as well as with current fully supervised deep learning-based methods.
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
20201 - Electrical and electronic engineering
Result continuities
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)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
ISSN
2329-9290
e-ISSN
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Volume of the periodical
30
Issue of the periodical within the volume
30
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
2295-2309
UT code for WoS article
000831126700006
EID of the result in the Scopus database
2-s2.0-85135228612