Automatic Speech Recognition

In this chapter we describe techniques to build a high performance speech recognizer for Arabic and related languages. The key insights are derived from our experience in the DARPA GALE program, a 5-year program devoted to enhancing the state-of-the-art in Arabic speech recognition and translation. The most important lesson is that general speech recognition techniques work very well also on Arabic. An example is the issue of vowelization: short vowels are often not transcribed in Arabic, Hebrew, and other Semitic languages. Semi-automatic vowelization procedures, specifically designed for the language, can improve the pronunciation lexicon. However, we also can simply choose to ignore the problem at the lexicon level, and compensate for the resulting pronunciation mismatch with the use of discriminative training of the acoustic models. While we focus on Arabic, in this chapter, we speculate that the vast majority of the issues we address here will completely carry over to other Semitic languages. We have tested the approaches discussed in this chapter only on Arabic, as that is the Semitic language with the most resources. Our experimental results demonstrate that such language-independent techniques can solve language-specific issues at least to a large extent. Another example is morphology, where we show that a combination of language-independent techniques (an efficient decoder to deal with large vocabulary and exponential language models) and language-specific techniques (a neural network language model that uses morphological and syntactic features) lead to good results. For these reasons we describe in the text a list of both language-independent and language-specific techniques. We describe also a full-fledged LVCSR system for Arabic that makes best use of all the techniques. We also demonstrate how this system can be used to bootstrap systems for related Arabic dialects and Semitic languages.