Speech emotional features measured by power-law distribution based on electroglottography

This study was designed to introduce a kind of novel speech emotional features extracted from Electroglot-tography (EGG). These features were obtained from the power-law distribution coefficient (PLDC) of fundamental frequency (F ₀) and duration parameters. First, the segments of silence, voiced and unvoiced (SUV) were distinguished by combining the EGG and speech information. Second, the F ₀ of voiced segment and the first-order differential of F ₀ was obtained by a cepstrum method. Third, PLDC of voiced segment as well as the pitch rise and pitch down duration were calculated. Simulation results show that the proposed features are closely connected with emotions. Experiments based on Support Vector Machine (SV M) are carried out. The results show that proposed features are better than those commonly used in the case of speaker independent emotion recognition.