Phonetic pronunciations for arabic speech-to-text systems

In this paper two aspects of generating and using phonetic arabic dictionaries are described. First, the use of single pronunciation acoustic models in the context of arabic large vocabulary automatic speech recognition (ASR) is investigated. These have been found to be useful for English ASR systems, when combined with standard multiple pronunciation systems. The second area examined is automatically deriving phonetic "pronunciations" for words that standard approaches, such as the Buckwalter morphological analyzer, cannot handle. Without pronunciations for these words the OOV rates for various Arabic tasks significantly increase. Here, pronunciations are automatically found by first deriving grapheme-to-phone rules, and associated rule probabilities. These are then used to produce the most likely pronunciation, or pronunciations, for any word. These approaches are evaluated on a large vocabulary arabic broadcast news and broadcast conversation transcription task. Both schemes are found to yield gains with a multi-pass/combination framework.