Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

✍ Scribed by Mohamed Afify; Yifan Gong; Jean-Paul Haton

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 223 KB
• Volume: 10
• Category: Article
• ISSN: 0885-2308
• DOI: 10.1006/csla.1996.0002

✦ Synopsis

In this work we extend our previously proposed stochastic mixture trajectory models to modelling time correlation. To achieve this extension we explicitly model the time evolution of an observed trajectory by the sum of a first order AR process and a mean component. This approach generalizes that employed in Digalakis et al., by using a mixture of trajectories to represent a phone in a parameter space. This generalization is necessary-from our experience-to account for different contextual variants of a phone. Optimum parameter estimates are obtained by two embedded EMalgorithms. Evaluated on an 850-word vocabulary continuous speech recognition task, the new method reduced the recognition error rate by about 25%.