Academic Journal
Robust automatic accent identification based on the acoustic evidence.
| Τίτλος: | Robust automatic accent identification based on the acoustic evidence. |
|---|---|
| Συγγραφείς: | Alsharhan, Eiman, Ramsay, Allan |
| Πηγή: | International Journal of Speech Technology; Sep2023, Vol. 26 Issue 3, p665-680, 16p |
| Θεματικοί όροι: | Automatic identification, Natural language processing, Artificial neural networks, Speech, Phoneme (Linguistics), Automatic speech recognition, Handwriting recognition (Computer science) |
| Περίληψη: | The paper describes a novel approach to automated accent identification by training a speech recogniser to distinguish between different versions of the phonemes that make up the language. In this approach, a standard speech recogniser is trained with data where the critical phonemes (vowels and a small set of consonants) are marked as coming in several varieties, one per accent. This is followed by inspection of the output of the recogniser to determine which versions predominate in a given utterance. Put simply, if a speaker produces phonemes that match Levantine versions of those phonemes they are characterised as speaking with a Levantine accent. Similarly, if they produce phonemes that match the Egyptian versions they should be characterised as speaking with an Egyptian accent, and so on. The accuracy of this approach to classifying speakers' accents varies from 79 to 86% when tested on speakers from the five main Arabic accent groups (Gulf, Iraqi, Egyptian, Levantine, Maghrebi), depending on a range of conditions discussed in the paper. These results are an improvement on the state of the art for accent recognition for Arabic, i.e. for classifying spoken, rather than written, material on the basis of the speaker's geographical origin. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Speech Technology 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10772-023-10031-2 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 173560836 RelevancyScore: 944 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 943.777770996094 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust automatic accent identification based on the acoustic evidence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alsharhan%2C+Eiman%22">Alsharhan, Eiman</searchLink><br /><searchLink fieldCode="AR" term="%22Ramsay%2C+Allan%22">Ramsay, Allan</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Speech Technology; Sep2023, Vol. 26 Issue 3, p665-680, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+identification%22">Automatic identification</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Phoneme+%28Linguistics%29%22">Phoneme (Linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Handwriting+recognition+%28Computer+science%29%22">Handwriting recognition (Computer science)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The paper describes a novel approach to automated accent identification by training a speech recogniser to distinguish between different versions of the phonemes that make up the language. In this approach, a standard speech recogniser is trained with data where the critical phonemes (vowels and a small set of consonants) are marked as coming in several varieties, one per accent. This is followed by inspection of the output of the recogniser to determine which versions predominate in a given utterance. Put simply, if a speaker produces phonemes that match Levantine versions of those phonemes they are characterised as speaking with a Levantine accent. Similarly, if they produce phonemes that match the Egyptian versions they should be characterised as speaking with an Egyptian accent, and so on. The accuracy of this approach to classifying speakers' accents varies from 79 to 86% when tested on speakers from the five main Arabic accent groups (Gulf, Iraqi, Egyptian, Levantine, Maghrebi), depending on a range of conditions discussed in the paper. These results are an improvement on the state of the art for accent recognition for Arabic, i.e. for classifying spoken, rather than written, material on the basis of the speaker's geographical origin. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Speech Technology 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: 10.1007/s10772-023-10031-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 665 Subjects: – SubjectFull: Automatic identification Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Speech Type: general – SubjectFull: Phoneme (Linguistics) Type: general – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Handwriting recognition (Computer science) Type: general Titles: – TitleFull: Robust automatic accent identification based on the acoustic evidence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alsharhan, Eiman – PersonEntity: Name: NameFull: Ramsay, Allan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 13812416 Numbering: – Type: volume Value: 26 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Speech Technology Type: main |
| ResultId | 1 |