Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder.
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| Title: | Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder. |
|---|---|
| Authors: | Sheferaw, Gebremichael Kibret1 (AUTHOR) gebrsh@gmail.com, Mwangi, Waweru1 (AUTHOR), Kimwele, Michael1 (AUTHOR), Mamuye, Adane2 (AUTHOR) |
| Source: | EURASIP Journal on Audio Speech & Music Processing. 1/20/2024, Vol. 2024 Issue 1, p1-17. 17p. |
| Subjects: | Adaptive modulation, Speech |
| Abstract: | Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Audio Speech & Music Processing is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174918052 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sheferaw%2C+Gebremichael+Kibret%22">Sheferaw, Gebremichael Kibret</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gebrsh@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mwangi%2C+Waweru%22">Mwangi, Waweru</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kimwele%2C+Michael%22">Kimwele, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mamuye%2C+Adane%22">Mamuye, Adane</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Audio+Speech+%26+Music+Processing%22">EURASIP Journal on Audio Speech & Music Processing</searchLink>. 1/20/2024, Vol. 2024 Issue 1, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Adaptive+modulation%22">Adaptive modulation</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Audio Speech & Music Processing is the property of Springer Nature 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: 10.1186/s13636-023-00325-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Adaptive modulation Type: general – SubjectFull: Speech Type: general Titles: – TitleFull: Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sheferaw, Gebremichael Kibret – PersonEntity: Name: NameFull: Mwangi, Waweru – PersonEntity: Name: NameFull: Kimwele, Michael – PersonEntity: Name: NameFull: Mamuye, Adane IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 01 Text: 1/20/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 16874714 Numbering: – Type: volume Value: 2024 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Audio Speech & Music Processing Type: main |
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