Abstract
Many languages, including Arabic, are characterized by a wide variety of different dialects that often differ strongly from each other. When developing speech technology for dialect-rich languages, the portability and reusability of data, algorithms, and system components becomes extremely important. In this paper, we describe the development of a large-vocabulary speech recognition system for Levantine Arabic, which was a new dialectal recognition task for our existing system. We discuss the dialect-specific modeling choices (grapheme vs. phoneme based acoustic models, automatic vowelization techniques, and morphological language models) and investigate to what extent techniques previously tested on other languages are portable to the present task. We present state-of-the-art recognition results on the 2004 Levantine Arabic Rich Transcription evaluation.
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