Context-dependent modeling in a segment-based speech recognition system

The goal of this thesis is to explore various strategies for incorporating contextual information into a segment-based speech recognition system, while maintaining computational costs at a level acceptable for implementation in a real-time system. The latter is achieved by using context-independent models in the search, while contextdependent models are reserved for re-scoring the hypotheses proposed by the contextindependent system. Within this framework, several types of context-dependent sub-word units were evaluated, including word-dependent, biphone, and triphone units. In each case, deleted interpolation was used to compensate for the lack of training data for the models. Other types of context-dependent modeling, such as context-dependent boundary modeling and \o set" modeling, were also used successfully in the re-scoring pass. The evaluation of the system was performed using the Resource Management task. Context-dependent segment models were able to reduce the error rate of the context-independent system by more than twenty percent, and context-dependent boundary models were able to reduce the word error rate by more than a third. A straight-forward combination of context-dependent segment models and boundary models leads to further reductions in error rate. So that it can be incorporated easily into existing and future systems, the code for re-sorting N -best lists has been implemented as an object in Sapphire, a framework for specifying the con guration of a speech recognition system using a scripting language. It is currently being tested on Jupiter, a real-time telephone based weather information system under development here at SLS.