Efficient compression method for pronunciation dictionaries

Pronunciation dictionaries are often used with other datadriven methods to model the pronunciations in phonemebased automatic speech recognition (ASR) and text-to-speech (TTS) systems. The dictionaries usually take a great amount of memory, which is a limiting factor in portable handheld devices. Compressing the pronunciation dictionaries results in minimal transmission bandwidth and less storage memory. In this paper we present a new procedure to efficiently compress pronunciation dictionaries. First, a novel method transforms the dictionary to a lower entropy representation. Second, the variability in the aligned pronunciation dictionary is reduced to further lower its entropy. Finally, generic lossless compression is applied on the transformed dictionary. Experiments were carried out on English names and words from US English CMU dictionary. The proposed scheme achieved 37.5% improvement over general-purpose lossless text compression.