Robust speech recognition using time boundary detection

This paper explores the benefits of including time boundary information in Hidden Markov Model based speech recognition systems. Traditional systems normally feed the parameterized data into the HMM recognizer, which result in relatively complicated models and computationally expensive search steps. We propose a few methods of detecting time boundaries prior to parameterization, and present a novel way of including this additional information in the recognizer. The result is significant simplification in the model prototypes, higher accuracy and faster performance.