Comparative analysis of gender identification using speech analysis and higher order statistics
Gender identification via speech processing is one of the hot research topics among the security research community. Many cyber systems are being developed to recognize human speech type. These systems mainly comprise of a feature segment process which extracts and selects the features of human spee...
| Main Authors: | , , , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IEEE
2019
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| Online Access: | http://irep.iium.edu.my/80383/ http://irep.iium.edu.my/80383/1/80383%20Comparative%20Analysis%20of%20Gender%20Identification.pdf http://irep.iium.edu.my/80383/2/80383%20Comparative%20Analysis%20of%20Gender%20Identification%20SCOPUS.pdf |
| _version_ | 1848788947240484864 |
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| author | Ahmad Qadri, Syed Asif Gunawan, Teddy Surya Wani, Taiba Alghifari, Muhammad Fahreza Mansor, Hasmah Kartiwi, Mira |
| author_facet | Ahmad Qadri, Syed Asif Gunawan, Teddy Surya Wani, Taiba Alghifari, Muhammad Fahreza Mansor, Hasmah Kartiwi, Mira |
| author_sort | Ahmad Qadri, Syed Asif |
| building | IIUM Repository |
| collection | Online Access |
| description | Gender identification via speech processing is one of the hot research topics among the security research community. Many cyber systems are being developed to recognize human speech type. These systems mainly comprise of a feature segment process which extracts and selects the features of human speeches. Feature extraction and feature selection are the most noteworthy phase of speech recognition involving numerous strategies. The purpose of this paper is to investigate the potential effectiveness of spectral analysis and higher-order statistics performed over the speech segments of different genders. Spectral analysis is done via spectral descriptors consisting of varied parameters which are widely used in machine learning applications. The varied gender speeches are distinguished by means of parameters, i.e., higher order statistics, like spectral centroid, spectral entropy, spectral kurtosis, spectral slope and spectral flatness. The results obtained show successful discrimination of male and female speeches based on the peakiness of speech, voiced and unvoiced and higher and lower formants. |
| first_indexed | 2025-11-14T17:48:55Z |
| format | Proceeding Paper |
| id | iium-80383 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:48:55Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-803832020-07-10T03:50:52Z http://irep.iium.edu.my/80383/ Comparative analysis of gender identification using speech analysis and higher order statistics Ahmad Qadri, Syed Asif Gunawan, Teddy Surya Wani, Taiba Alghifari, Muhammad Fahreza Mansor, Hasmah Kartiwi, Mira T Technology (General) Gender identification via speech processing is one of the hot research topics among the security research community. Many cyber systems are being developed to recognize human speech type. These systems mainly comprise of a feature segment process which extracts and selects the features of human speeches. Feature extraction and feature selection are the most noteworthy phase of speech recognition involving numerous strategies. The purpose of this paper is to investigate the potential effectiveness of spectral analysis and higher-order statistics performed over the speech segments of different genders. Spectral analysis is done via spectral descriptors consisting of varied parameters which are widely used in machine learning applications. The varied gender speeches are distinguished by means of parameters, i.e., higher order statistics, like spectral centroid, spectral entropy, spectral kurtosis, spectral slope and spectral flatness. The results obtained show successful discrimination of male and female speeches based on the peakiness of speech, voiced and unvoiced and higher and lower formants. IEEE 2019-04-06 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/80383/1/80383%20Comparative%20Analysis%20of%20Gender%20Identification.pdf application/pdf en http://irep.iium.edu.my/80383/2/80383%20Comparative%20Analysis%20of%20Gender%20Identification%20SCOPUS.pdf Ahmad Qadri, Syed Asif and Gunawan, Teddy Surya and Wani, Taiba and Alghifari, Muhammad Fahreza and Mansor, Hasmah and Kartiwi, Mira (2019) Comparative analysis of gender identification using speech analysis and higher order statistics. In: 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2019), 27 - 29 Aug 2019, Kuala Lumpur, Malaysia. https://ieeexplore.ieee.org/document/9057296 10.1109/ICSIMA47653.2019.9057296 |
| spellingShingle | T Technology (General) Ahmad Qadri, Syed Asif Gunawan, Teddy Surya Wani, Taiba Alghifari, Muhammad Fahreza Mansor, Hasmah Kartiwi, Mira Comparative analysis of gender identification using speech analysis and higher order statistics |
| title | Comparative analysis of gender identification using speech analysis and higher order statistics |
| title_full | Comparative analysis of gender identification using speech analysis and higher order statistics |
| title_fullStr | Comparative analysis of gender identification using speech analysis and higher order statistics |
| title_full_unstemmed | Comparative analysis of gender identification using speech analysis and higher order statistics |
| title_short | Comparative analysis of gender identification using speech analysis and higher order statistics |
| title_sort | comparative analysis of gender identification using speech analysis and higher order statistics |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/80383/ http://irep.iium.edu.my/80383/ http://irep.iium.edu.my/80383/ http://irep.iium.edu.my/80383/1/80383%20Comparative%20Analysis%20of%20Gender%20Identification.pdf http://irep.iium.edu.my/80383/2/80383%20Comparative%20Analysis%20of%20Gender%20Identification%20SCOPUS.pdf |