Assessing text-to-phoneme mapping strategies in speaker independent isolated word recognition

A phonetic transcription of the vocabulary, i.e., a lexicon, is needed in sub-word based speech recognition and text-to-speech systems. Decision trees and neural networks have successfully been used for creating lexicons on-line from an open vocabulary. We briefly review these methods and compare them in detail in the text-to-phoneme mapping task as part of a phoneme based speaker independent speech recognizer. The decision tree and neural network based methods were first evaluated in terms of phoneme accuracy and then in extensive speech recognition tests. American english dictionaries and speech databases were used in all experiments. The decision tree based method achieved high phoneme accuracies when the training material covered the test vocabulary well. In typical speech recognition tests, the recognition rates obtained using the decision tree based lexicons were close to the baseline that was obtained using accurate transcriptions. Although the lexicons obtained using neural networks resulted in somewhat lower baseline recognition rates, they provided slightly better results in generalization tests. Moreover, when the neural network based mappings were appended with a look-up table comprising the most likely vocabulary items, which would be the practical set-up, their performance increased significantly. The main advantage of neural networks over decision trees is their low memory consumption.