Abstract
The speech signals are non-stationary processes with changes in time and frequency. The structure of a speech signal is also affected by the presence of several paralinguistics phenomena such as emotions, pathologies, cognitive impairments, among others. Non-stationarity can be modeled using several parametric techniques. A novel approach based on time dependent auto-regressive moving average TARMA is proposed here to model the non-stationarity of speech signals. The model is tested in the recognition of fear-typeo emotions in speech. The proposed approach is applied to model syllables and unvoiced segments extracted from recordings of the Berlin and enterface05 databases. The results indicate that TARMA models can be used for the automatic recognition of emotions in speech.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
