Title :
Emotion Classification of Infant Voice Based on Features Derived from Teager Energy Operator
Author :
Gao, Hui ; Chen, Shanguang ; Su, Guangchuan
Abstract :
To study effective speech features which can represent different emotion styles in infant voice, nonlinear features based on Teager Energy Operator are investigated. Neutral state and 4 emotional states (i.e. happiness, impatience, anger and fear) are classified from the infant voice database. MFCC extraction and HMM-based emotion classification are used as baseline system to evaluate the emotional classification performance of nonlinear features. In comparison with MFCC, relative improvements which are 2%, 2% , 2% and 10% of classification capacity are obtained when using NFD_Mel , AF_Mel, DAF_Mel and TEO_SBCC. But the performance of emotion classification decreases respectively by 14% for using AM_SBCC.
Keywords :
Emotion recognition; Frequency domain analysis; Mel frequency cepstral coefficient; Pediatrics; Psychology; Signal processing; Spatial databases; Speech analysis; Speech processing; Speech recognition; Teager energy operator; classification; emotion; speech;
Conference_Titel :
Image and Signal Processing, 2008. CISP '08. Congress on
Conference_Location :
Sanya, China
Print_ISBN :
978-0-7695-3119-9
DOI :
10.1109/CISP.2008.623