Basic linear algebra, calculus, probability theory
Objectif du cours
Speech and natural language processing is a subfield of artificial intelligence used in an increasing number of applications; yet, while some aspects are on par with human performances, others are lagging behind. This course will present the full stack of speech and language technology, from automatic speech recognition to parsing and semantic processing. The course will present, at each level, the key principles, algorithms and mathematical principles behind the state of the art, and confront them with what is know about human speech and language processing. Students will acquire detailed knowledge of the scientific issues and computational techniques in automatic speech and language processing and will have hands on experience in implementing and evaluating the important algorithms.
- speech features & signal processing
- hidden markov & finite state modeling
- probabilistic parsing
- continuous embeddings
- deep learning for language-related tasks (DNNs, RNNs)
- linguistics and psycholinguistics
- comparing human and machine performance
Organisation des séances
The courses consist in 9 three-hours blocks, followed by an oral project presentation on week 10. Everything will be remote this year. Each three hours block consists in two off-line hours where you watch prerecorded materials (available on youtube), and one hour of interactive remote session taking place on Fridays, from 4pm to 5pm (zoom).
The interactive session will have the following structure:
- 4:00pm- 4:30pm: on-line QUIZZ.
- 4:30pm- 5:00pm: Q&A session.
Mode de validation
The validation is in two parts:
on-line QUIZZ (40% of the total grade).
Project. (60% of the total grade).
The recommended, but not obligatory textbook for the course is D. Jurafsky & J. Martin – Speech and Language Processing, 3rd (online) edition for already available chapters [J&M3], 2nd edition otherwise [J&M2]. Readings for each of the sessions will be provided by the instructors.
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