Abstract

In order to help understand why gains in pronunciation modeling have proven so elusive, we investigated which kinds of pronunciation variation are well captured by triphone models, and which are not. We do this by examining the change in behavior of a recognizer as it receives further triphone training. We show that many of the kinds of variation which previous pronunciation models attempt to capture, including phone substitution or phone reduction, are in fact already well captured by triphones. Our analysis suggests new areas where future pronunciation models should focus, including syllable deletion. 1. INTRODUCTION Many studies of human-to-human speech have shown that pronunciation variation is a key factor contributing to the high error rates of current recognizers. For example [1] showed that Switchboard word error decreased from 40% to 8% if the dictionary pronunciation matched the actual pronunciation. While the need for better pronunciation modeling is widely acknowledged, ...

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