Organisation des séances

Eight courses (2h) and 6 practical assignments (QAs for 1 hour) based around the implementation of key algorithms. For the assignments, students are provided with the necessary data and Python code and will hand in their source code and a max two page report, detailing their work, the difficulties encountered and the results.

1. Speech and language processing: Principles and applications (2h)

Presentation of the main speech and language processing stack, main computational challenges, and main practical applications.

No Assignement Readings

2. Speech I: Acoustic modeling (2h + 1h)

Algorithms: signal processing, speech features, source separation, GMMs, DNNs

Human processing: auditory neuroscience, psychoacoustics, speech perception, scene analysis

Assignment. Evaluating Speech Features

Given a set of modules (filterbank, compressor, expander, spectrogram), perform ABX on different combinations and evaluate classification performance.

Readings

J&M2 7.1-7.4, 9.3

3. Speech II: Language modeling (2h+1h)

Algorithms: HMM decoding: forward, Viterbi, RNN, LSTM, end-to-end CTC, embeddings. Human processing: speaker and accent adaptation, L2 perception

Application speech: application orthographique

Assignment. HMM Decoder

Given trained GMM-HMM, build a decoder and evaluate phone error rate on test set

Readings

J&M2 9.1-9.8

4. Language I: Formal Grammars and Syntax (2h+1)

Algorithms: Chomsky’s hierarchy, Finite state transducers, Context Free grammars, Mildly context sensitive formalisms

Human processing: linguistic typology

Assignment: POS Tagger

Given a training data, build a Hidden Markov Part-of-Speech tagger and evaluate accuracy on test set.

Readings

J&M3, ch. 9 & 10

5. Language II: Parsing (2h+1h)

Algorithms: PCFGs, treebanks, estimation, chart & dependency parsing Human processing: syntax, garden-paths, acquisition, dependency, hierarchy

Assignment: Probabilistic Parsing

Given a treebank, extract a PCFG, implement a CKY parser and evaluate parsing f1-score on a test set.

Readings

M. Covington (2001) – A Fundamental Algorithm for Dependency Parsing: http://www.stanford.edu/~mjkay/covington.pdf

J&M3 ch. 12 & 13

6. Language III: Language Processing in the wild (2h+1h)

Algorithms: text normalization, coreference, distributional semantics, word embeddings

Human processing: conversational & casual language

Assignment: Evaluating Topic Models

Given a dataset of documents and human topic annotation, correlate different topic models with human judgements.

Readings:

J. Chang, J. Boyd-Graber, C. Wang, S. Gerrish, and D. Blei (2009). Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems. http://www.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf

7.     Language IV: Lost in translation (2h+1h)

Algorithms: CNNs, sequence to sequence RNNs, attentional models, graph models Human processing:

Assignment:

Test find systematic patterns of errors in Google Translate

8.     Open questions and hot topics (2h)

–  linguistic and non linguistic context

– Unsupervised learning

–  Domain adaptation & zero shot learning