The aim of this study is to develop a speech recognition system for Turkish broadcast news. State-of-the-art speech recognition systems utilize statistical models. A large amount of data is required to reliably estimate these models. For this study, a large Turkish Broadcast News database, consisting of the speech signal and corresponding transcriptions, is being collected. In this paper, information about this database and experiments performed using the system developed on the collected data are presented. In addition to the baseline system, various sub-word language models are investigated. Lexical stem-endings are proposed as a novel unit for language modeling and are shown to perform better than surface stem-endings and morphs. Currently, our best systems have lower than 20% error on clean speech.
Bibliographic reference. Arısoy, Ebru / Sak, Haşim / Saraçlar, Murat (2007): "Language modeling for automatic turkish broadcast news transcription", In INTERSPEECH-2007, 2381-2384.