Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Deokjin | - |
dc.contributor.author | Oh, Heung-Seon | - |
dc.contributor.author | Jung, Yuchul | - |
dc.date.accessioned | 2021-08-12T02:40:15Z | - |
dc.date.available | 2021-08-12T02:40:15Z | - |
dc.date.created | 2021-08-12 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19375 | - |
dc.description.abstract | With the expanding development of on-device artificial intelligence, voice-enabled devices such as smart speakers, wearables, and other on-device or edge processing systems have been proposed. However, building or obtaining large training datasets that are essential for robust keyword spotting (KWS) remains cumbersome. To address this problem, we propose a deep neural network that can rapidly establish a high-performance KWS system from arbitrary keyword instruction sets. We use an encoder pretrained with a large-scale speech corpus as the backbone network and then design an effective transfer network for KWS. To demonstrate the feasibility of the proposed network, various experiments were conducted on Google Speech Command Datasets V1 and V2. In addition, to verify the applicability of the network for different languages, we conducted experiments using three different Korean speech command datasets. The proposed network outperforms state-of-the-art deep neural networks in both experiments. Furthermore, the proposed network can understand real human voice even when trained with synthetic text-to-speech data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Seo, Deokjin | - |
dc.contributor.affiliatedAuthor | Jung, Yuchul | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3078715 | - |
dc.identifier.wosid | 000673945000001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.80682 - 80691 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 80682 | - |
dc.citation.endPage | 80691 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Convolution | - |
dc.subject.keywordAuthor | Transfer learning | - |
dc.subject.keywordAuthor | Speech recognition | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Convolutional codes | - |
dc.subject.keywordAuthor | Keyword spotting | - |
dc.subject.keywordAuthor | speech commands recognition | - |
dc.subject.keywordAuthor | transfer learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.