TY - GEN
T1 - Age and gender classification for permission control of mobile devices in tracking systems
AU - Choukri, Merahi
AU - Wu, Shaochuan
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019 Published by Springer Nature Switzerland AG 2019. All Rights Reserved.
PY - 2019
Y1 - 2019
N2 - Not only does the human voice provide the semantics of the spoken words but also it contains the speaker-dependent characteristics, such as the gender, the age, and the emotional state of the speaker. In the last decade, speech recognition gained a great interest in identifying and tracking systems. According to the speech length of ten to thirty seconds, this paper proposes an age and gender classification method for permission control of mobile devices. Each speech signal is firstly extracted to 40 features by Mel Frequency cepstral Coefficients (MFCC). After that, the Support Vector Machine (SVM) is used to finish the age and gender classification. This paper studies six kernel models of SVM and concludes that cubic, quadratic, and medium Gaussian kernel models could improve the recognition rate up to 93.75%, 91.25% and 93.75% respectively. Therefore, it is promising for permission control of a mobile in tracking systems.
AB - Not only does the human voice provide the semantics of the spoken words but also it contains the speaker-dependent characteristics, such as the gender, the age, and the emotional state of the speaker. In the last decade, speech recognition gained a great interest in identifying and tracking systems. According to the speech length of ten to thirty seconds, this paper proposes an age and gender classification method for permission control of mobile devices. Each speech signal is firstly extracted to 40 features by Mel Frequency cepstral Coefficients (MFCC). After that, the Support Vector Machine (SVM) is used to finish the age and gender classification. This paper studies six kernel models of SVM and concludes that cubic, quadratic, and medium Gaussian kernel models could improve the recognition rate up to 93.75%, 91.25% and 93.75% respectively. Therefore, it is promising for permission control of a mobile in tracking systems.
KW - Classification
KW - Mel Frequency cepstral Coefficients (MFCC)
KW - Permission control
KW - Support Vector Machine (SVM)
UR - https://www.scopus.com/pages/publications/85069186171
U2 - 10.1007/978-3-030-22971-9_28
DO - 10.1007/978-3-030-22971-9_28
M3 - 会议稿件
AN - SCOPUS:85069186171
SN - 9783030229702
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 318
EP - 324
BT - Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings
A2 - Han, Shuai
A2 - Ye, Liang
A2 - Meng, Weixiao
PB - Springer Verlag
T2 - 1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019
Y2 - 25 May 2019 through 26 May 2019
ER -