Modulation features for speech recognition

Automatic speech recognition (ASR) systems can benefit from including into their acoustic processing part new features that account for various nonlinear and time-varying phenomena during speech production. In this paper, we develop robust methods to extract novel acoustic features from speech signals of the modulation type based on time-varying models for speech analysis. Further, we integrate the new speech features with the standard linear ones (mel-frequency cesptrum) to develop a augmented set of acoustic features and demonstrate its efficacy by showing significant improvements in HMM-based word recognition over the TIMIT database.