Modelling of pronunciation variability is an important part of the acoustic model of a speech recognition system. Good pronunciation models contribute to the robustness and portability of a speech recogniser. Usually pronunciation modelling is associated with the recognition lexicon which allows a direct control of HMM selection. However, in state-of-the-art systems the use of clustering techniques has considerable cross-effects for the dictionary design. Most large vocabulary speech recognition systems make use of a dictionary with multiple possible pronunciation variants per word. In this paper a method for a consistent reduction of the number of pronunciation variants to one pronunciation per word is described. Using the single pronunciation dictionaries similar or better word error rate performance is achieved both onWall Street Journal and Switchboard data.
Cite as: Hain, T. (2002) Implicit pronunciation modelling in ASR. Proc. ITRW on Pronunciation Modeling and Lexicon Adaptation for Spoken Language Technology (PMLA 2002), 129-134
@inproceedings{hain02_pmla,
author={Thomas Hain},
title={{Implicit pronunciation modelling in ASR}},
year=2002,
booktitle={Proc. ITRW on Pronunciation Modeling and Lexicon Adaptation for Spoken Language Technology (PMLA 2002)},
pages={129--134}
}