Short-Utterance Embedding Enhancement Method Based on Time Series Forecasting Technique for Text-Independent Speaker Verification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jeong-Hwan | - |
dc.contributor.author | Yang, Joon-Young | - |
dc.contributor.author | Chang, Joon-Hyuk | - |
dc.date.accessioned | 2022-07-06T10:15:11Z | - |
dc.date.available | 2022-07-06T10:15:11Z | - |
dc.date.created | 2022-04-06 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139507 | - |
dc.description.abstract | Short-utterance embedding, which is a speaker embedding extracted from a short utterance, shows poor speaker verification performance due to insufficient speaker information. To address the problem, we propose a method to map the set of short-utterance embeddings to a set of long-utterance embeddings based on a neural network. Specifically, a speech utterance is cropped into multiple segments whose durations are gradually increasing, and the speaker embeddings are extracted from the sequence of cropped segments using a pre-trained speaker embedding extractor. Subsequently, the sequence of embeddings is divided into a group of short-utterances embeddings and that of long-utterance embeddings. In our method, a sequence-to-sequence model based forecasting technique is employed, where an encoder transforms the group of short-utterance embeddings to a fixed-dimensional vector, and then a decoder converts the vector into a group of long-utterance embeddings. Experimental results on the VoxCeleb and Speakers in the Wild datasets show that our method improves the text-independent speaker verification performance under short utterance condition. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | Short-Utterance Embedding Enhancement Method Based on Time Series Forecasting Technique for Text-Independent Speaker Verification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chang, Joon-Hyuk | - |
dc.identifier.doi | 10.1109/ASRU51503.2021.9688156 | - |
dc.identifier.scopusid | 2-s2.0-85126802527 | - |
dc.identifier.wosid | 000792364700018 | - |
dc.identifier.bibliographicCitation | 2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings, pp.130 - 137 | - |
dc.relation.isPartOf | 2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings | - |
dc.citation.title | 2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings | - |
dc.citation.startPage | 130 | - |
dc.citation.endPage | 137 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Embeddings | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Time series | - |
dc.subject.keywordPlus | Speech recognition | - |
dc.subject.keywordPlus | Embeddings | - |
dc.subject.keywordPlus | Forecasting techniques | - |
dc.subject.keywordPlus | Neural-networks | - |
dc.subject.keywordPlus | Performance | - |
dc.subject.keywordPlus | Short durations | - |
dc.subject.keywordPlus | Short-duration speaker verification | - |
dc.subject.keywordPlus | Speaker verification | - |
dc.subject.keywordPlus | Text-independent speaker verification | - |
dc.subject.keywordPlus | Time series forecasting | - |
dc.subject.keywordPlus | Time series forecasting models | - |
dc.subject.keywordAuthor | short-duration speaker verification | - |
dc.subject.keywordAuthor | Text-independent speaker verification | - |
dc.subject.keywordAuthor | time series forecasting model | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9688156 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
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.