Topological invariants as speech features for automatic speech recognition

Abstract

The article presents topological invariants as speech features for speech recognition systems based on hidden Markov models. A short introduction is provided to the mathematical concept of topological invariants and space symmetries for the speech recognition problem. This involves a basic overview of the relevant auditory characteristic and its modelling in order to identify possible symmetries and invariants. Once the concept is derived, several of its modifications vital for HMM systems such as reduction of dimensions, within–class feature decorrelation and a signal plane rotation are presented and evaluated on a real system. The final system is evaluated and compared to other features using both context–dependent and context–independent models. Tests were accomplished on the professional speech database, where the achieved accuracies reached up to 97.7%, 98.7% and 98.9% for string of digits, application words and isolated digits tests, respectively.