Waikiki, Hawaii - During the Eighth Conference of the Association for Machine Translation in the Americas (AMTA), AppTek Chief Scientist Hassan Sawaf, Ph.D., will discuss best practices for implementing Hybrid Machine Translation to improve media monitoring translations in fluency, context and accuracy. His presentation entitled, "Hybrid Machine Translation Applied to Media Monitoring," will outline how a Hybrid Machine Translation approach delivers better results than a pure rule-based and a pure corpus-based approach for both written and spoken input. This presentation will also demonstrate how to increase language model quality for dialect language speech recognition by using non-dialect, non-spontaneous language resources.

Hassan Sawaf has over 10 years experience in the areas of Natural Language Processing, Machine Translation and Speech Recognition. He was project manager for Daimler Benz in Germany, senior researcher at the University of Aachen, where he did his graduate studies for his Diploma (1992-1998) and Ph.D. (1998-2003), and currently heads Research & Development for AppTek. Under his guidance as the head  of R&D, AppTek recently introduced the first hybrid machine translation system that fully integrated two complete machine translation systems (statistical and rule-based) into one system that provides the performance and benefits of both approaches. His group also led the development of numerous automated speech recognition engines, which have been integrated into AppTek's numerous media monitoring systems.  

 Hilton Prince Kuhio Hotel

Waikiki, Hawaii

http://www.amtaweb.org/AMTA2008.html

Friday, October 24, 2008

ABOUT APPTEKAppTek is a developer of human language technology products with a complete suite for text and speech (voice) processing and recognition. The Company also leads major research and development efforts to further the advancement in the field of developing better methods and technologies in the field of HLT. AppTek's product offerings include machine translation (MT) and automatic speech recognition (ASR) for a growing list of more than 23 languages; multilingual information retrieval with query and topic search capabilities; name-finding applications; and integrated suites providing automatic speech recognition and machine translation in media monitoring of broadcast and telephony speech as well as handheld and wearable speech-to-speech translation devices.

www.apptek.com

SOURCE: AppTek

AppTek

Chris Leach, 703-218-3555 This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Copyright Business Wire 2008 

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