Search

We present a large scale effort to build a commercial Automatic Speech Recognition (ASR) product for Arabic. Our goal is to support voice search, dictation, and voice control for the general Arabic-speaking public, including support for multiple Arabic dialects. We describe our ASR system design and compare recognizers for five Arabic dialects, with the potential to reach more than 125 million people in Egypt, Jordan, Lebanon, Saudi Arabia, and the United Arab Emirates (UAE). We compare systems built on diacritized vs. non-diacritized text. We also conduct cross-dialect experiments, where we train on one dialect and test on the others. Our average word error rate (WER) is 24.8% for voice search. [ABSTRACT FROM PUBLISHER]

We describe the Arabic broadcast transcription system fielded by IBM in the GALE Phase 5 machine translation evaluation. Key advances over our Phase 4 system include a new Bayesian Sensing HMM acoustic model; multistream neural network features; a MADA vowelized acoustic model; and the use of a variety of language model techniques with significant additive gains. These advances were instrumental in achieving a word error rate of 7.4% on the Phase 5 evaluation set, and an absolute improvement of 0.9% word error rate over our 2009 system on the unsequestered Phase 4 evaluation data. [ABSTRACT FROM PUBLISHER]

We report a series of experiments about how we can progress from Modern Standard Arabic (MSA) to Levantine ASR, in the context of the GALE DARPA program. While our GALE models achieved very low error rates, we still see error rates twice as high when decoding dialectal data. In this paper, we make use of a state-of-the-art Arabic dialect recognition system to automatically identify Levantine and MSA subsets in mixed speech of a variety of dialects including MSA. Training separate models on these subsets, we show a significant reduction in word error rate over using the entire data set to train one system for both dialects. During decoding, we use a tree array structure to mix Levantine and MSA models automatically using the posterior probabilities of the dialect classifier as soft weights. This technique allows us to mix these models without sacrificing performance for either varieties. Furthermore, using the initial acoustic-based dialect recognition system's output, we show that we can bootstrap a text-based dialect classifier and use it to identify relevant text data for building Levantine language models. Moreover, we compare different vowelization approaches when transitioning from MSA to Levantine models. [ABSTRACT FROM PUBLISHER]