Implicit modelling of pronunciation variation in automatic speech recognition

Abstract

Modelling of pronunciation variability is an important task for the acoustic model of an automatic speech recognition system. Good pronunciation models contribute to the robustness and generic applicability of a speech recogniser. Usually pronunciation modelling is associated with a lexicon that allows to explicitly control the selection of appropriate HMMs for a particular word. However, the use of data-driven clustering techniques or specific parameter tying techniques has considerable impact on this form of model selection and the construction of a task-optimal dictionary. Most large vocabulary speech recognition systems make use of a dictionary with multiple possible pronunciation variants per word. By manual addition of pronunciation variants explicit human knowledge is used in the recognition process. For reasons of complexity the optimisation of manual entries for performance is often not feasible. In this paper a method for the stepwise reduction of the number of pronunciation variants per word to one is described. By doing so in a way consistent with the classification procedure, pronunciation variation is modelled implicitly. It is shown that the use of single pronunciation dictionaries provides similar or better word error rate performance, achieved both on Wall Street Journal and Switchboard data. The use of single pronunciation dictionaries in conjunction with Hidden Model Sequence Models as an example of an implicit pronunciation modelling technique shows further improvements.