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Preparing for the 2008 Beijing Olympics : The LingTour and KNOWLISTICS projects

Preparing for the 2008 Beijing Olympics : The LingTour and KNOWLISTICS projects

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Preparing for the 2008 Beijing Olympics : The LingTour and KNOWLISTICS projects

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  1. Preparing for the 2008 Beijing Olympics :The LingTour and KNOWLISTICS projects MAO Yuhang, DING Xiao-Qing, NI Yang, LIN Shiuan-Sung, Laurence LIKFORMAN, Christian BOITET, Gérard CHOLLET, Alain GOYE, Eric LECOLINET, Jacques PRADO Presented here by Gérard CHOLLETchollet@tsi.enst.fr GET-ENST/CNRS-LTCIhttp://www.tsi.enst.fr/~chollet

  2. Outline • Rationale of the proposal • Objectives • The Beijing 2008 Olympics • Approaches • Multimedia, multilingual information server • Information kiosk • Intelligent Camera • Bilingual Voice Communicator • Needs and relevance • A PDA for tourists and travelling businessmen • Conclusions and Perspectives

  3. Rationale for the IP-KNOWLISTICS • Logistics for knowledge in a specific domain (OG) • Language independent knowledge representation and management • Multimedia (text, speech, image, video) • Multimodal access (text, speech, pen, visual I/O) • Distributed multilingual, multimedia server accessible from mobile terminals (phone, PDA, PC,…) and kiosks • Primarily targetted for tourist applications initially • 2008 Beijing Olympics as a field trial

  4. Technical developments • Language independent knowledge representation (using conceptual graphs and an Intermediate Representation Language like the ‘Universal Networking Language’) • Tools for enconversion and evaluation • Generation in 12 target languages • Multilingual Speech Synthesis and Recognition • VoiceUNL-based interactive dialog agent • ‘Intelligent camera’ with Chinese character recognition • Cross-language ‘Multimodal communicator’ on a PDA • Cross-language lexical access

  5. Chinese character recognition

  6. capture reco translation Intelligent camera from Tsinghua Univ.

  7. Extracting text from scene images • Complex color images • Uncontrolled illumination • Variations : size, fonts, orientation, texture • Complex backgrounds, shadows

  8. Text extraction • Searching for character regions (text has uniform color) • Multi-channel decomposition • Connected components analysis • Grouping of components • Alignment analysis (number of horizontally or vertically aligned components) • Text identification (language independant features : size, alignment,…) • Detection rate : 84 % • False alarm rate : 5.6 %

  9. Cross-language Multimodal Communicator • Use of a visual display (e.g. on a PDA) to mediate the dialogue between 2 persons speaking different languages. • Recognition of short utterances, display of a word graph, selection of keywords, visualisation (and synthesis) of the translation of key words and groups of words. • Specialised lexicon for dialog acts in typical touristic situations (in a restaurant, at the hotel, medical assitance, in the street, in public transport, about the Olympic games,…) • UMTS access to an information server offering maps, photographs, video sequences, web browsing, …

  10. Sharedacousticmodels Automatic Speech Recognition in Multiple Languages • Sharing of acoustic models between languages to simplify extensibility to other languages. • Combination of phone models and adaptation from small amounts of data in new languages. • Model adaptation to user and environmental situations. Chinese French Language specific models

  11. Knowledge representation • A formal language for representating the meaning of natural language sentence. • UNL (Universal Networking Language) introduced to describe natural language semantics. • Language-independent context indexing for cross-language information retrieval. • Use of conceptual hierarchy of UNL to address the inherent ambiguity of natural languages. • A set of semantic relations (linking concepts together) for a structured information pattern.

  12. UNL representation “The cat drank the milk” can be encoded by: agt(drink(icl>do,agt>thing, obj>liquid).@past.@entry, cat(icl>mammal>animal).@def) obj(drink(icl>do,agt>thing, obj>liquid).@past.@entry, milk(icl>beverage>food).@def) agt, obj are binary semantic relations

  13. Role of semantic contents representation in indexing User’s request AudioVideo Textual Digital UNL encoding Cross lingual Multimedia platform User specific information UNL decoding

  14. Application architecture UMTS server Access information a word graph,+ a list of keywords Translation Speech synthesis

  15. Digital OlympicMulti-Language Information Network Service System Project

  16. From VoiceXML to VoiceUNL and MultimediaUNL. Presented here by Gérard CHOLLETchollet@tsi.enst.fr ENST/CNRS-LTCIhttp://www.tsi.enst.fr/~chollet With the contribution of Christian BOITET, Mutsuko TOMOYIKO and Catherine PELACHAUD

  17. Outline • Rationale of the proposition • Objectives • Promotion of a new standard, demonstrations • Approaches • An extra layer of VoiceXML • Need and relevance • Multilingual Vocal Servers • Integration and structuring effect • Conclusions and Perspectives

  18. Rationale for VoiceUNL • Need for Language Independent Vocal Servers,  Need fora language independent knowledge representation and management formalism • Principle of proposed solution: • Start from UNL graphs augmented with voice-oriented semantic marks (special UWs, attributes), • Generate in the target language, • Voice-oriented marks become prosodic markers, • Final conversion to VoiceXML • 2008 Beijing Olympics as a field trial

  19. What is VoiceXML ? • A recommendation of W3C (WWW Consortium) • An extension of XML for vocal information servers, • A set of normalised markup tags, • Current ags concern language identification, voice prompting, speech synthesis, form filling, barge in, echo cancelling,… • No provision to access a semantically encoded data base, • Need for a UNL-type front-end • Compatibility with MPEG4-SNHC (talking head)

  20. Applications

  21. Prosodic information in UNL • Attributes that can influence the grammatical and the prosodic structure of a sentence already exist: • @emphasis • @qfocus • Representations should be defined, concerning : • Emotion: @angry, @bored, @relaxed…? • Focus: grouping words to emphasize in a scope? • Passivity: @passive? • Speaker: @age, @sex, special UWs for voice characteristics…? • Expression (for face and gesture animation): special UWs/constructs?

  22. Conclusions and Perspectives • Demonstrations to be prepared within the LingTour, Normalangue and KNOWLISTICS projects • First target is the Beijing 2008 Olympics • Some concept-oriented formalism (such as Sowa's conceptual graphs) may be used to store knowledge • before building in UNL "interlingual prelinguistic, communicative content"

  23. Conclusions and Perspectives • UNL representation of meaning of natural language sentences directly available for retrieval, indexing and knowledge extraction. • UNL with multimedia contents (text, speech, image, video)and multimodal access (text, speech, visual I/O) to enrich the service for communication. • Comprehensive and extensive information service on PDAs with access to UMTS and wireless LAN.