Speech analysis and feature extraction using chaotic models
Nonlinear systems based on chaos theory can model various aspects of the nonlinear dynamic phenomena occuring during speech production. In this paper, we explore modem methods and algorithms from chaotic systems theory for modeling speech signals in a multidimensional phase space and for extracting nonlinear acoustic features. Further, we integrate these chaotic-type features with the standard linear ones (based on cepstrum) to develop a generalized hybrid set of short-time acoustic features for speech signals and demonstrate its efficacy by showing significant improvements in HMM-based word recognition.