Spoken Conversational Interface for Language Learning (SCILL)
SCILL is a collaborative research project between the MIT CSAIL Spoken Language Systems Group and the Cambridge Machine Intelligence Laboratory, whose goal is to provide a framework for practicing conversation in a language being learned through interactive dialogue with a computer.

The project is funded within the framework of the Pervasive Computing Knowledge Integration Community (KIC), over the time interval from November 2003 to September 2006.


  • Web-based Demo System for Practicing Mandarin Chinese in the Flight Domain
  • Video clip illustrating spoken interaction with weather domain spoken dialogue system
  • Video clip illustrating language practice in flight domain
  • Audio clip illustrating spoken interaction in weather domain
  • Audio clip illustrating spoken language translation game in flight domain
  • Project Aims

    The aim of this project is to develop a language learning system with four major components:

    In practice, the target language will be Mandarin Chinese and the native language will be US or UK English. However, the system design will as far as possible be language independent.

    The system will enable a student to participate in a dialogue with the system in Mandarin whilst simultaneously having access to a "tutor" that could tell them how to say certain phrases. For example, the topic might be about the weather in a particular city, and the bi-lingual "tutor" would provide the student with helpful hints on how to communicate in Mandarin with the system. This allows the student to engage in practice conversation in a non-threatening environment. The student would be able to gauge their success by the degree to which the system understood their Mandarin queries, and the number of times they needed to consult the "tutor" for translation advice.

    A later off-line interaction using the assessment component would allow the student to re-examine their speech. The system could provide feedback on their overall pronunciation quality, as well as identifying words that were poorly enunciated, allowing them to compare their pronunciation with a standard. The system could also conceivably repair the tone production, while preserving the overall quality of the student's voice.


    Key Project Personnel

    MIT

    Cambridge

    Presentation and Publications

    Presentations:

    Papers:
    Thesis: