Contract type : Public service fixed-term contract
Renewable contract : Oui
Level of qualifications required : Graduate degree or equivalent
Fonction : Tempary Research Position
Within the framework of a partnership
- project fund : CNRS and an industry partner
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.
[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, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques”, These de doctorat en Informatique, Université de Lorraine, 2016.
- 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.
Strong background in mathematics, machine learning (DNN), statistics
Following profiles are welcome, either:
- Strong background in signal processing
- Strong experience with natural language processing
Excellent English writing and speaking skills are required in any case.
- Subsidised catering service
- Partially-reimbursed public transport
- Social security
- Paid leave
- Flexible working hours
- Sports facilities
- Town/city : Villers-lès-Nancy
- Inria Center : CRI Nancy - Grand Est
- Starting date : 2019-04-01
- Duration of contract : 1 year
- Deadline to apply : 2019-04-30
- Inria Team : MULTISPEECH
Illina Irina / firstname.lastname@example.org
Inria, the French national research institute for the digital sciences, promotes scientific excellence and technology transfer to maximise its impact. It employs 2,400 people. Its 200 agile project teams, generally with academic partners, involve more than 3,000 scientists in meeting the challenges of computer science and mathematics, often at the interface of other disciplines. Inria works with many companies and has assisted in the creation of over 160 startups. It strives to meet the challenges of the digital transformation of science, society and the economy.
Instruction to apply
Defence Security :
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
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