Using word temporal structure in HMM speech recognition

Isolated word speech recognizers with fixed vocabularies are often used to provide vocal services through the telephone line. The paper illustrates a simple postprocessing approach that allows the hypotheses produced by a hidden Markov model recognizer to be rescored taking into account the global temporal structure of the pronounced words. Our approach does not directly rely on state/word duration modeling. It models, instead, the global time variations of the spectral features of each word and their correlation in time: two important perceptual cues that are only partially exploited by standard HMMs. This method has been evaluated using three isolated word speaker independent systems with vocabulary of different size and complexity. We show that, with minimal overhead, the recognition performance improves not only for small vocabulary recognition systems such as the isolated digit one, or for the recognition of 26 Italian spelling names, but also for a system with a 475 city name vocabulary included in a vocal service that provides information about the main railway connections