Dynamic lexicon using phonetic features

In order to better model pronunciation variations, we present in this paper a method to build a lexicon whose content changes dynamically with the input speech. To achieve this goal, we proceeded in two steps. In the first step, a static augmented lexicon is created by adding new phone transcriptions to a basic lexicon. These new variants are derived from phonetic features that are automatically extracted from some training speech. Then in the second step, phonetic features are extracted again during recognition and help to select entries in the augmented lexicon that best match the phonetic characteristics of a given speech. These selected transcriptions constitute the dynamic lexicon, which is specific to each input utterance. Experiments showed a 16.0% relative reduction in WER compared to the baseline and 16.7% compared to when a static augmented lexicon is used.