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The Arabic language presents a number of challenges for speech recognition, arising in part from the significant differences in the spoken and written forms, in particular the conventional form of texts being non-vowelized. Being a highly inflected language, the Arabic language has a very large lexical variety and typically with several possible (generally semantically linked) vowelizations for each written form. This article summarizes research carried out over the last few years on speech-to-text transcription of broadcast data in Arabic. The initial research was oriented toward processing of broadcast news data in Modern Standard Arabic, and has since been extended to address a larger variety of broadcast data, which as a consequence results in the need to also be able to handle dialectal speech. While standard techniques in speech recognition have been shown to apply well to the Arabic language, taking into account language specificities help to significantly improve system performance.

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The majority of state-of-the-art speech recognition systems make use of system combination. The combination approaches adopted have traditionally been tuned to minimising Word Error Rates (WERs). In recent years there has been growing interest in taking the output from speech recognition systems in one language and translating it into another. This paper investigates the use of cross-site combination approaches in terms of both WER and impact on translation performance. In addition the stages involved in modifying the output from a Speech-to-Text (STT) system to be suitable for translation are described. Two source languages, Mandarin and Arabic, are recognised and then translated using a phrase-based statistical machine translation system into English. Performance of individual systems and cross-site combination using cross-adaptation and ROVER are given. Results show that the best STT combination scheme in terms of WER is not necessarily the most appropriate when translating speech.