“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
