Leveraging laryngograph data for robust voicing detection in speech.
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| Title: | Leveraging laryngograph data for robust voicing detection in speech. |
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| Authors: | Zhang, Yixuan1 (AUTHOR) zhang.7388@osu.edu, Wang, Heming1 (AUTHOR) wang.11401@osu.edu, Wang, DeLiang1,2 (AUTHOR) dwang@cse.ohio-state.edu |
| Source: | Journal of the Acoustical Society of America. Nov2024, Vol. 156 Issue 5, p3502-3513. 12p. |
| Subjects: | Machine learning, Signal processing, Source code, Data recorders & recording, Generalization, Deep learning |
| Abstract: | Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications. While conventional signal processing methods and deep learning algorithms have been proposed for this task, their need to fine-tune threshold parameters for different datasets and limited generalization restrict their utility in real-world applications. To address these challenges, this study proposes a supervised voicing detection model that leverages recorded laryngograph data. The model, adapted from a recently developed CrossNet architecture, is trained using reference voicing decisions derived from laryngograph datasets. Pretraining is also investigated to improve the generalization ability of the model. The proposed model produces robust voicing detection results, outperforming other strong baseline methods, and generalizes well to unseen datasets. The source code of the proposed model with pretraining is provided along with the list of used laryngograph datasets to facilitate further research in this area. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 181208034 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Leveraging laryngograph data for robust voicing detection in speech. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yixuan%22">Zhang, Yixuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhang.7388@osu.edu</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Heming%22">Wang, Heming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wang.11401@osu.edu</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+DeLiang%22">Wang, DeLiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dwang@cse.ohio-state.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Acoustical+Society+of+America%22">Journal of the Acoustical Society of America</searchLink>. Nov2024, Vol. 156 Issue 5, p3502-3513. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Source+code%22">Source code</searchLink><br /><searchLink fieldCode="DE" term="%22Data+recorders+%26+recording%22">Data recorders & recording</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications. While conventional signal processing methods and deep learning algorithms have been proposed for this task, their need to fine-tune threshold parameters for different datasets and limited generalization restrict their utility in real-world applications. To address these challenges, this study proposes a supervised voicing detection model that leverages recorded laryngograph data. The model, adapted from a recently developed CrossNet architecture, is trained using reference voicing decisions derived from laryngograph datasets. Pretraining is also investigated to improve the generalization ability of the model. The proposed model produces robust voicing detection results, outperforming other strong baseline methods, and generalizes well to unseen datasets. The source code of the proposed model with pretraining is provided along with the list of used laryngograph datasets to facilitate further research in this area. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1121/10.0034445 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 3502 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Source code Type: general – SubjectFull: Data recorders & recording Type: general – SubjectFull: Generalization Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Leveraging laryngograph data for robust voicing detection in speech. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Yixuan – PersonEntity: Name: NameFull: Wang, Heming – PersonEntity: Name: NameFull: Wang, DeLiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00014966 Numbering: – Type: volume Value: 156 – Type: issue Value: 5 Titles: – TitleFull: Journal of the Acoustical Society of America Type: main |
| ResultId | 1 |
