Multilanguage speech-based gender classification using time-frequency features and SVM classifier
Speech is the most significant communication mode among human beings and a potential method for human-computer interaction (HCI). Being unparallel in complexity, the perception of human speech is very hard. The most crucial characteristic of speech is gender, and for the classification of gender oft...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Book Chapter |
| Language: | English English English |
| Published: |
Springer
2021
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/86116/15/Presentation%20Schedule%20iCITES2020%202nd.pdf http://irep.iium.edu.my/86116/21/86116_Multilanguage%20speech-based%20gender%20classification.pdf http://irep.iium.edu.my/86116/27/86116_Multilanguage%20speech-based%20gender%20classification_SCOPUS.pdf http://irep.iium.edu.my/86116/ https://icites2020.ump.edu.my/index.php/en/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.iium.irep.86116 |
|---|---|
| record_format |
dspace |
| spelling |
my.iium.irep.861162021-05-11T02:31:48Z http://irep.iium.edu.my/86116/ Multilanguage speech-based gender classification using time-frequency features and SVM classifier Wani, Taiba Gunawan, Teddy Surya Mansor, Hasmah Ahmad Qadri, Syed Asif Sophian, Ali Ambikairajah, Eliathamby Ihsanto, Eko TK7885 Computer engineering Speech is the most significant communication mode among human beings and a potential method for human-computer interaction (HCI). Being unparallel in complexity, the perception of human speech is very hard. The most crucial characteristic of speech is gender, and for the classification of gender often pitch is utilized. However, it is not a reliable method for gender classification as in numerous cases, the pitch of female and male is nearly similar. In this paper, we propose a time-frequency method for the classification of gender-based on the speech signal. Various techniques like framing, Fast Fourier Transform (FFT), auto-correlation, filtering, power calculations, speech frequency analysis, and feature extraction and formation are applied on speech samples. The classification is done based on features derived from the frequency and time domain processing using the Support Vector Machines (SVM) algorithm. SVM is trained on two speech databases Berlin Emo-DB and IITKGP-SEHSC, in which a total of 400 speech samples are evaluated. An accuracy of 83% and 81% for IITKGP-SEHSC and Berlin Emo-DB have been observed, respectively. Springer 2021 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/86116/15/Presentation%20Schedule%20iCITES2020%202nd.pdf application/pdf en http://irep.iium.edu.my/86116/21/86116_Multilanguage%20speech-based%20gender%20classification.pdf application/pdf en http://irep.iium.edu.my/86116/27/86116_Multilanguage%20speech-based%20gender%20classification_SCOPUS.pdf Wani, Taiba and Gunawan, Teddy Surya and Mansor, Hasmah and Ahmad Qadri, Syed Asif and Sophian, Ali and Ambikairajah, Eliathamby and Ihsanto, Eko (2021) Multilanguage speech-based gender classification using time-frequency features and SVM classifier. In: Springer’s Advances in Intelligent Systems and Computing (AISC). Springer, pp. 1-10. https://icites2020.ump.edu.my/index.php/en/ |
| institution |
Universiti Islam Antarabangsa Malaysia |
| building |
IIUM Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
International Islamic University Malaysia |
| content_source |
IIUM Repository (IREP) |
| url_provider |
http://irep.iium.edu.my/ |
| language |
English English English |
| topic |
TK7885 Computer engineering |
| spellingShingle |
TK7885 Computer engineering Wani, Taiba Gunawan, Teddy Surya Mansor, Hasmah Ahmad Qadri, Syed Asif Sophian, Ali Ambikairajah, Eliathamby Ihsanto, Eko Multilanguage speech-based gender classification using time-frequency features and SVM classifier |
| description |
Speech is the most significant communication mode among human beings and a potential method for human-computer interaction (HCI). Being unparallel in complexity, the perception of human speech is very hard. The most crucial characteristic of speech is gender, and for the classification of gender often pitch is utilized. However, it is not a reliable method for gender classification as in numerous cases, the pitch of female and male is nearly similar. In this paper, we propose a time-frequency method for the classification of gender-based on the speech signal. Various techniques like framing, Fast Fourier Transform (FFT), auto-correlation, filtering, power calculations, speech frequency analysis, and feature extraction and formation are applied on speech samples. The classification is done based on features derived from the frequency and time domain processing using the Support Vector Machines (SVM) algorithm. SVM is trained on two speech databases Berlin Emo-DB and IITKGP-SEHSC, in which a total of 400 speech samples are evaluated. An accuracy of 83% and 81% for IITKGP-SEHSC and Berlin Emo-DB have been observed, respectively. |
| format |
Book Chapter |
| author |
Wani, Taiba Gunawan, Teddy Surya Mansor, Hasmah Ahmad Qadri, Syed Asif Sophian, Ali Ambikairajah, Eliathamby Ihsanto, Eko |
| author_facet |
Wani, Taiba Gunawan, Teddy Surya Mansor, Hasmah Ahmad Qadri, Syed Asif Sophian, Ali Ambikairajah, Eliathamby Ihsanto, Eko |
| author_sort |
Wani, Taiba |
| title |
Multilanguage speech-based gender classification using time-frequency features and SVM classifier |
| title_short |
Multilanguage speech-based gender classification using time-frequency features and SVM classifier |
| title_full |
Multilanguage speech-based gender classification using time-frequency features and SVM classifier |
| title_fullStr |
Multilanguage speech-based gender classification using time-frequency features and SVM classifier |
| title_full_unstemmed |
Multilanguage speech-based gender classification using time-frequency features and SVM classifier |
| title_sort |
multilanguage speech-based gender classification using time-frequency features and svm classifier |
| publisher |
Springer |
| publishDate |
2021 |
| url |
http://irep.iium.edu.my/86116/15/Presentation%20Schedule%20iCITES2020%202nd.pdf http://irep.iium.edu.my/86116/21/86116_Multilanguage%20speech-based%20gender%20classification.pdf http://irep.iium.edu.my/86116/27/86116_Multilanguage%20speech-based%20gender%20classification_SCOPUS.pdf http://irep.iium.edu.my/86116/ https://icites2020.ump.edu.my/index.php/en/ |
| _version_ |
1701162769664966656 |
| score |
13.211869 |