Academic Journal
Spontaneous Speech and Its Features Are Taken into Account When Creating Recognition Programs.
| Τίτλος: | Spontaneous Speech and Its Features Are Taken into Account When Creating Recognition Programs. |
|---|---|
| Συγγραφείς: | Yergaliyev, Askhat, Sharipbay, Altynbek, Baibulekova, Lyailya |
| Πηγή: | Ingénierie des Systèmes d'Information; Apr2023, Vol. 28 Issue 2, p443-450, 8p |
| Θεματικοί όροι: | Research, Deep learning |
| Περίληψη: | The relevance of the stated subject of this scientific research is determined by the numerous difficulties of spontaneous speech recognition due to the presence of a complex of unrelated factors, as well as the need to find the best ways to overcome them through recognition programs that are used in the development of a multilingual corpus. This research aims to identify and study the key features of spontaneous speech that should be considered when creating recognition programs. The basis of the methodological approach in this scientific study is a combination of methods combining a systematic analysis of the principles of building a multilingual corpus with an analytical study of the principles of operation of spontaneous speech recognition programs. In the course of this scientific study, results were obtained that indicate a significant increase in the practical efficiency of automatic speech recognition systems when introducing sound signal correction algorithms, which opens up additional opportunities for developing such software. In addition, the results of this scientific study clearly demonstrate the significant impact of the accuracy of the effects of speech recognition programs on the quality of development of multilingual corpora, which include relatively large volumes of texts. The practical significance of the results obtained in the course of this scientific work, as well as the conclusions formulated on their basis, lies in the possibility of their use in the development of a multilingual corpus and spontaneous speech recognition programs for their subsequent use in various information systems, to obtain results related to the need accurate decoding of automatic speech, taking into account all its characteristic features. [ABSTRACT FROM AUTHOR] |
| Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 164291963 RelevancyScore: 932 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 931.952514648438 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spontaneous Speech and Its Features Are Taken into Account When Creating Recognition Programs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yergaliyev%2C+Askhat%22">Yergaliyev, Askhat</searchLink><br /><searchLink fieldCode="AR" term="%22Sharipbay%2C+Altynbek%22">Sharipbay, Altynbek</searchLink><br /><searchLink fieldCode="AR" term="%22Baibulekova%2C+Lyailya%22">Baibulekova, Lyailya</searchLink> – Name: TitleSource Label: Source Group: Src Data: Ingénierie des Systèmes d'Information; Apr2023, Vol. 28 Issue 2, p443-450, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The relevance of the stated subject of this scientific research is determined by the numerous difficulties of spontaneous speech recognition due to the presence of a complex of unrelated factors, as well as the need to find the best ways to overcome them through recognition programs that are used in the development of a multilingual corpus. This research aims to identify and study the key features of spontaneous speech that should be considered when creating recognition programs. The basis of the methodological approach in this scientific study is a combination of methods combining a systematic analysis of the principles of building a multilingual corpus with an analytical study of the principles of operation of spontaneous speech recognition programs. In the course of this scientific study, results were obtained that indicate a significant increase in the practical efficiency of automatic speech recognition systems when introducing sound signal correction algorithms, which opens up additional opportunities for developing such software. In addition, the results of this scientific study clearly demonstrate the significant impact of the accuracy of the effects of speech recognition programs on the quality of development of multilingual corpora, which include relatively large volumes of texts. The practical significance of the results obtained in the course of this scientific work, as well as the conclusions formulated on their basis, lies in the possibility of their use in the development of a multilingual corpus and spontaneous speech recognition programs for their subsequent use in various information systems, to obtain results related to the need accurate decoding of automatic speech, taking into account all its characteristic features. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) 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: 10.18280/isi.280220 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 443 Subjects: – SubjectFull: Research Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Spontaneous Speech and Its Features Are Taken into Account When Creating Recognition Programs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yergaliyev, Askhat – PersonEntity: Name: NameFull: Sharipbay, Altynbek – PersonEntity: Name: NameFull: Baibulekova, Lyailya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 16331311 Numbering: – Type: volume Value: 28 – Type: issue Value: 2 Titles: – TitleFull: Ingénierie des Systèmes d'Information Type: main |
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