MMI training for continuous phoneme recognition on the TIMIT database

This paper reports our experiences with a phoneme recognition system for the TIMIT database which uses multiple mixture continuous density monophone HMMs trained using MMI. A comprehensive set of results are presented comparing the ML and MMI training criteria for both diagonal and full covariance models. These results using simple monophone HMMs show clear performance gains achieved by MMI training, and are comparable to the best reported by others including those which use context-dependent models. In addition, the paper discusses a number of performance and implementation issues which are crucial to successful MMI training.

A new, fast response CO2 sensor

The inability of emissions reduction methods to meet upcoming legislation without an unacceptable increase in vehicle cost is a major problem of automobile manufacturer. This work aims to develop a cost-effective reduction of automobile emissions. A prototype CO2 sensor with 5 msec response time was built and bench tested, then used on an engine. The sensor design was based on standard emissions measurement technology using non-dispersive IR absorption. An improved sensor has now been completed with significant improvements in terms of signal to noise ratio and long-term stability. The improved sensor will be used to measure CO2 concentrations on three different engines. The results will then be used to validate engine and catalyst models and to propose control strategies aimed at reducing overall emissions. A brief description of the sensor itself was presented. Original is an abstract.