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“Human Language Technology (HLT) makes it easier for people to interact with machines. This can benefit a wide range of people – from illiterate farmers in remote villages who want to obtain relevant medical information over a cellphone, to scientists in state-of-the-art laboratories who want to focus on problem-solving with computers.”

Human Language Technology studies some different areas;

Multimodal Interaction
Technologies to deal with a recent paradigm shift in the design of Pattern Recognition, where the traditional concept of full-automation is being changed to systems where the decision process is conditioned by human feedback. Problems and applications considered within this area include: Relevance-based (image) information retrieval and Interactive-Predictive processing for Computer Assited Machine Translation, as well as for the Interactive Transcription of speech audio streems and text images.
Machine Translation
Speech-to-speech translation or text-to-text translation for limited domains. Finite-state and statistical transducers are used as the basis of the machine translation systems. These models can be learnt automatically from real examples of translation. Applications: translation of technical reports, hotel services, etc.
Handwritten Cursive Text Recognition (HTR)
Both off-line (document images) and on-line HTR (tablet or e-pen signals) are considered. No prior character or word segmentation is needed. Technology, borrowed from Speech Recognition, relies on character Hidden Markov Models, Finite State word models, and syntactic N-Grams. After model training, for each given text line image, a holistic (“Viterbi”) search provides both an optimal transcription and the corresponding word and character segmentations. Applications: Transcription of ancient and legacy documents, transcription of unconstrained handwritten text in survey forms, etc.
Automatic Speech Recognition and Understanding
The speech utterances are decoded into strings of words or into strings of semantic units. Finite-state grammars are used as the basis of such systems. These finite-state grammars are learnt automatically from real examples of utterances or text. Applications: telephone exchange services, device control by voice, information queries, etc.
Image Analysis and Computer Vision
Identification of the objects in an image. Statistical and Syntactic Pattern recognition techniques are used. Applications: OCR and document analysis, medical diagnosis, fingerprint identification, classification of chromosomes, aids for the handicapped, manufacturing quality control, etc.

REFERENCES:

 *Meraka Institute. Retrieved 25th May 2009, 12:43 from: http://www.meraka.org.za/humanLanguage.htm

*Pattern recognition and Human Language Technologies. Retrieved 25th May 2009, 12:44 from: http://prhlt.iti.es/content.php?page=areas.php

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