LECTRA - Rich Transcription of Lectures for E-Learning Applications: Difference between revisions
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== Publications ==
== Publications ==
Isabel Trancoso, Ricardo
Isabel Trancoso, Ricardo Nunes, Luís Neves, [http://www.inesc-id.pt/pt/indicadores/Ficheiros/3169.pdf Classroom Lecture Recognition], In Computational Processing of the Portuguese Language: 7th International Workshop, PROPOR 2006, Springer, pages 190 - 199, May 2006
== Demos ==
== Demos ==
Revision as of 08:21, 29 June 2006
Producing automatic transcriptions of classroom lectures may be important for both e-learning and e-inclusion purposes.
The greatest research challenge is the recognition of spontaneous speech (error rate much higher than for read speech). Even human produced transcriptions would be very difficult to understand because of the absence of punctuation and the presence of disfluencies (filled pauses, repetitions, hesitations, false starts, etc.). Hence, one has to enrich the speech transcription by adding information about sentence boundaries and speech disfluencies.
Sponsored by: FCT (POSC/PLP/58697/2004)
Start: March 2005
Duration: 2 years
Project Leader: Isabel Trancoso
- João Neto
- Diamantino Caseiro
- M. Céu Viana (CLUL)
- Ricardo Nunes
- Rui Amaral
- Hugo Meinedo
- Luís Oliveira
- Fernando Batista
This project is done with the cooperation of IMMI (Intelligent MultiModal Interfaces), led by Prof. Joaquim Jorge.
The goal of this project is the production of multimedia lecture contents for e-learning applications. We shall take as a pilot study a course for which the didactic material (e.g. text book, problems, viewgraphs) is already electronically available and in Portuguese. This is an increasingly more frequent situation, namely in technical courses. Our contribution to these contents will be to add, for each lecture in the course, the recorded video signal and the synchronized lecture transcription. We believe that this synchronized transcription may be specially important for hearing-impaired students.
The project will encompass 5 main tasks. In the first one we shall collect the training and test material (both in terms of recorded audio-video signals and textual data) related to this course. In the second task we shall use this training data to adapt the acoustic, lexical and language models of our large vocabulary continuous speech recognizer to the course domain, thus yielding a first transcription of the lecture contents. The third task has as a goal to "enrich" this transcription with metadata that would render it more intelligible. Given the state of the art in terms of metadata extraction and the comparatively low recognition rate for spontaneous speech relative to read speech, this task is the one where the main research challenge resides. The fourth task deals with integrating the recorded audio-video and corresponding transcription with the other multimedia contents and synchronize them according to topic, so that a student may browse through the contents, seeing a viewgraph, the corresponding part in the text book, and the audio-video with the corresponding lecture transcription as caption. The final task is user evaluation for which we intend to use a panel of both normal hearing and hearing impaired students. For the latter, we shall evaluate two types of lecture transcription: with and without manual correction. This later evaluation will give us an indication of how close we are in terms of automatic lecture transcription to be able to use such tools in real-time in a classroom.
- T1 - Data collection
- T2 - Model adaptation
- T3 - Spontaneous speech recognition
- T4 - Integration of lecture transcription with other multimedia conetnts
- T5 - User evaluation
Isabel Trancoso, Ricardo Nunes, Luís Neves, C. Viana, H. Moniz, D. Caseiro, A. Isabel Mata, Recognition of Classroom Lectures in European Portuguese, In Proc. INTERSPEECH 2006, Pittsburgh, September 2006
Isabel Trancoso, Ricardo Nunes, Luís Neves, Classroom Lecture Recognition, In Computational Processing of the Portuguese Language: 7th International Workshop, PROPOR 2006, Springer, pages 190 - 199, May 2006
This demo shows the result of the application of our Broadcast News recognizer after adaptation of the acoustic, lexical and language models to the course domain (Production of Multimedia Contents).