Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers.
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| Title: | Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers. |
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| Authors: | Singhal, Abhishek1 (AUTHOR), Sharma, Devendra Kumar1 (AUTHOR) |
| Source: | Journal of Engineering (2314-4912). 12/15/2022, p1-9. 9p. |
| Subjects: | Fisher discriminant analysis, Speech synthesis, Voice analysis, Voice culture, Support vector machines, Gender, Food recall |
| Abstract: | This paper presents the estimation of accuracy in male, female, and transgender identification using different classifiers with the help of voice signals. The recall value of each gender is also calculated. This paper reports the third gender (transgender) identification for the first time. Voice signals are the most appropriate and convenient way to transfer information between the subjects. Voice signal analysis is vital for accurate and fast identification of gender. The Mel Frequency Cepstral Coefficients (MFCCs) are used here as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature that configures the gender identification system. Recurrent Neural Network–Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) are utilized as classifiers in this work. In the proposed models, the experimental result does not depend on the text of the speech, the language of the speakers, and the time duration of the voice samples. The experimental results are obtained by analyzing the common voice samples. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a k-fold value of 5. The recall value of genders and accuracy of the proposed models also varied according to the number of voice samples in training and testing datasets. The highest accuracy for gender identification is found as 94.44%. The simulation results show that the accuracy of the RNN is always found at a higher value than SVM and LDA. The gender-wise highest recall value of the proposed model is 95.63%, 96.71%, and 97.22% for males, females, and transgender, respectively, using voice signals. The recall value of the transgender is high in comparison to other genders. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell 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: 160821624 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singhal%2C+Abhishek%22">Singhal, Abhishek</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharma%2C+Devendra+Kumar%22">Sharma, Devendra Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 12/15/2022, p1-9. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fisher+discriminant+analysis%22">Fisher discriminant analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+synthesis%22">Speech synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Voice+analysis%22">Voice analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Voice+culture%22">Voice culture</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Gender%22">Gender</searchLink><br /><searchLink fieldCode="DE" term="%22Food+recall%22">Food recall</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents the estimation of accuracy in male, female, and transgender identification using different classifiers with the help of voice signals. The recall value of each gender is also calculated. This paper reports the third gender (transgender) identification for the first time. Voice signals are the most appropriate and convenient way to transfer information between the subjects. Voice signal analysis is vital for accurate and fast identification of gender. The Mel Frequency Cepstral Coefficients (MFCCs) are used here as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature that configures the gender identification system. Recurrent Neural Network–Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) are utilized as classifiers in this work. In the proposed models, the experimental result does not depend on the text of the speech, the language of the speakers, and the time duration of the voice samples. The experimental results are obtained by analyzing the common voice samples. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a k-fold value of 5. The recall value of genders and accuracy of the proposed models also varied according to the number of voice samples in training and testing datasets. The highest accuracy for gender identification is found as 94.44%. The simulation results show that the accuracy of the RNN is always found at a higher value than SVM and LDA. The gender-wise highest recall value of the proposed model is 95.63%, 96.71%, and 97.22% for males, females, and transgender, respectively, using voice signals. The recall value of the transgender is high in comparison to other genders. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell 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: 10.1155/2022/9291099 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: Fisher discriminant analysis Type: general – SubjectFull: Speech synthesis Type: general – SubjectFull: Voice analysis Type: general – SubjectFull: Voice culture Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Gender Type: general – SubjectFull: Food recall Type: general Titles: – TitleFull: Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singhal, Abhishek – PersonEntity: Name: NameFull: Sharma, Devendra Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: 12/15/2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 23144904 Titles: – TitleFull: Journal of Engineering (2314-4912) Type: main |
| ResultId | 1 |
