Application of Neural Networks in Emotional Speech Recognition
被引:0
作者:
Bojanic, Milana
论文数: 0引用数: 0
h-index: 0
机构:
Univ Novi Sad, Fac Tech Sci, Novi Sad 21000, SerbiaUniv Novi Sad, Fac Tech Sci, Novi Sad 21000, Serbia
Bojanic, Milana
[1
]
Crnojevic, Vladimir
论文数: 0引用数: 0
h-index: 0
机构:
Univ Novi Sad, Fac Tech Sci, Novi Sad 21000, SerbiaUniv Novi Sad, Fac Tech Sci, Novi Sad 21000, Serbia
Crnojevic, Vladimir
[1
]
Delic, Vlado
论文数: 0引用数: 0
h-index: 0
机构:
Univ Novi Sad, Fac Tech Sci, Novi Sad 21000, SerbiaUniv Novi Sad, Fac Tech Sci, Novi Sad 21000, Serbia
Delic, Vlado
[1
]
机构:
[1] Univ Novi Sad, Fac Tech Sci, Novi Sad 21000, Serbia
来源:
ELEVENTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING (NEUREL 2012)
|
2012年
关键词:
emotional speech recognition;
neural network;
D O I:
暂无
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
Emotional speech recognition (ESR) from the aspect of human-machine interaction (HCI) is a prerequisite for the framework of interacting partners within the HCI. This paper addresses the application of neural network (NN) in ESR. The performance of NN is tested using three different feature sets which are basis for ESR: prosodic features, spectral features and a set of their combination. The results of these feature sets are compared using several network topologies and two training algorithms. It has been shown that using joint prosodic-spectral feature set as input to three layer feed-forward NN trained with back-propagation algorithm has the best performance in 5-class emotional speech recognition task.