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Abstract:

Today's recognizers are primarily based on single pronunciations for most words. This means that the burden of modeling phonetic variability falls entirely on acoustic modeling. In addition, certain types of pronunciation variation (phone deletion/reduction, dialect) are impossible to model well at the acoustic level. We suspect that one of the difficulties in recognizing conversational speech (compared to read speech) is the greater variability of pronunciation. We propose to capture this variability by modeling the pronunciations for each word. The goal of this project is to automatically learn a model of word pronunciation from data. We focus on frequent words that appear many times in the Switchboard and Callhome corpora, since a small number of words make up a large fraction of the total errors. We can hope to learn these pronunciations automatically since these words occur many times in the training data. All past attempts in this area have treated pronunciation variants as mutually independent, i.e., under the assumption that any speaker would choose one of the given variants with a given probability, independent of related choices in the same phonological context, conversation, by the same speaker. Such an approach is simple to implement, but increases the number of parameters and the

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