Shi, Y., Huang, Q. and Hain, T. orcid.org/0000-0003-0939-3464 (2021) H-VECTORS : improving the robustness in utterance-level speaker embeddings using a hierarchical attention model. Neural Networks, 142. pp. 329-339. ISSN 0893-6080
Abstract
In this paper, a hierarchical attention network is proposed to generate robust utterance-level embeddings (H-vectors) for speaker identification and verification. Since different parts of an utterance may have different contributions to speaker identities, the use of hierarchical structure aims to learn speaker related information locally and globally. In the proposed approach, frame-level encoder and attention are applied on segments of an input utterance and generate individual segment vectors. Then, segment level attention is applied on the segment vectors to construct an utterance representation. To evaluate the quality of the learned utterance-level speaker embeddings on speaker identification and verification, the proposed approach is tested on several benchmark datasets, such as the NIST SRE2008 Part1, the Switchboard Cellular (Part1), the CallHome American English Speech ,the Voxceleb1 and Voxceleb2 datasets. In comparison with some strong baselines, the obtained results show that the use of H-vectors can achieve better identification and verification performances in various acoustic conditions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Neural Networks. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Speaker embeddings; Hierarchical attention; Speaker identification; Speaker verification; Attention mechanism |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Innovate UK 104264 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Aug 2022 13:41 |
Last Modified: | 23 May 2023 00:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.neunet.2021.05.024 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190286 |