Automatically generated word pronunciations from phoneme classifier output

An automatic procedure for modeling alternative pronunciations of words produced by different talkers is described. The research compared recognition performance on forty city and state names using three different representations of each word. In the first case, the expected pronunciation(s) of each word was produced by an expert. In the second case, a dynamic programming algorithm was used to create a pronunciation network for each word by combining phonetic transcriptions from ten utterances of the word produced by human labelers. The third case was identical to the second, except that the phonetic labels were provided automatically by a phonetic recognition algorithm. On a test set of words produced by new speakers, equivalent recognition performance was observed for the pronunciation networks derived from human and machine labels. Both produced performance superior to that obtained with the pronunciations produced by the expert.<>