Nonlinear dynamical system based acoustic modeling for ASR

The work presented is centered around a speech production model called the chained dynamical system model (CDSM) which is motivated by the fundamental limitations of the mainstream ASR approaches. The CDSM is essentially a smoothly time varying continuous state nonlinear dynamical system, consisting of two sub dynamical systems coupled as a chain so that one system controls the parameters of the next system. The speech recognition problem is posed as inverting the CDSM, for which we propose a solution based on the theory of embedding. The resulting architecture, which we call inverted CDSM (ICDSM) is evaluated in a set of experiments involving a speaker independent, continuous speech recognition task on the TIMIT database. Results of these experiments which can be compared with the corresponding results in the literature, confirm the feasibility and advantages of the approach