Connectionists: Research engineer or post-doc position in Natural Language Processing (LORIA, France)

[Irina Illina]

Research engineer or post-doc position in Natural Language Processing:
Introduction of semantic information in a speech recognition system

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

Team: Multispeech, LORIA-INRIA (https://team.inria.fr/multispeech/)

Contact: illina at loria.fr, dominique.fohr at loria.fr

Duration: 12-15 months

Deadline to apply : December 20th, 2019

Required skills: Strong background in mathematics, machine learning (DNN),
statistics, natural language processing and computer program skills (Perl,
Python).

Following profiles are welcome, either:
         Strong background in signal processing
or
         Strong experience with natural language processing
Excellent English writing and speaking skills are required in any case.

Candidates should email a detailed CV with diploma and to apply on 
http://jobs.inria.fr/public/classic/en/offres/

LORIA is the French acronym for the “Lorraine Research Laboratory in Computer
Science and its Applications” and is a research unit (UMR 7503), common to
CNRS, the University of Lorraine and INRIA. This unit was officially created in
1997. Loria’s missions mainly deal with fundamental and applied research in
computer sciences.

MULTISPEECH is a joint research team between the Université of Lorraine, Inria,
and CNRS. Its research focuses on speech processing, with particular emphasis
to multisource (source separation, robust speech recognition), multilingual
(computer assisted language learning), and multimodal aspects (audiovisual
synthesis).

Context and objectives

Under noisy conditions, audio acquisition is one of the toughest challenges to
have a successful automatic speech recognition (ASR). Much of the success
relies on the ability to attenuate ambient noise in the signal and to take it
into account in the acoustic model used by the ASR. Our DNN (Deep Neural
Network) denoising system and our approach to exploiting uncertainties have
shown their combined effectiveness against noisy speech.

The ASR stage will be supplemented by a semantic analysis. Predictive
representations using continuous vectors have been shown to capture the
semantic characteristics of words and their context, and to overcome
representations based on counting words. Semantic analysis will be performed by
combining predictive representations using continuous vectors and uncertainty
on denoising. This combination will be done by the rescoring component. All our
models will be based on the powerful technologies of DNN.

The performances of the various modules will be evaluated on artificially noisy
speech signals and on real noisy data. At the end, a demonstrator, integrating
all the modules, will be set up.

Main activities
• study and implementation of a noisy speech enhancement module and a
propagation of uncertainty module;
• design a semantic analysis module;
• design a module taking into account the semantic and uncertainty information.

References

[Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN
uncertainty propagation using GMM-derived uncertainty features for noise robust
ASR, IEEE Signal Processing Letters, 2018.

[Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN
uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic
Speech Recognition and Understanding Workshop, 2017.

[Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio
source separation with deep neural networks. IEEE/ACM Transactions on Audio,
Speech, and Language Processing, 2016.

[Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word
importance with the neural bag-of-words model, in Proc. ACL Representation
Learning for NLP (Repl4NLP) Workshop, Aug 2016.

[Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J.
Efficient estimation of word representations in vector space, CoRR, vol.
abs/1301.3781, 2013.