Contrat postdoctoral: Evolutive end-to-end neural networks for speaker recognition


Host Laboratory : LIUM, équipe LST,
Site : Le Mans University, France
Supervisor : Anthony Larcher (LIUM)
Duration : 24 months, Starting date : asap, end of Jaune 2020 at the latest

Mots clef : Deep Learning, evolutive networks, genetic algorithms, explainability, speaker recognition, end-to-end


The LST team from LIUM (Le Mans University) is focusing on evolutive end-to-end neural networks for speaker recognition. The Extensor project (French ANR funded) aims at developing novel architectures for end-to-end speaker recognition as well as explaining the behavior of those networks. The focus of Extensor is threefold:

  1. get rid of the legacy of bayesian system’s architecture and explore wider opportunities offered in deep learning;
  2. explore real end-to-end architectures exploiting the tax signal instead of classical features (such as MFCC of filterbanks);
  3. Develop tools for explainability in speaker recognition.


1. Develop end-to-end speaker recognition system based on state-of-the-art approaches (xvectors, sincnet…)

2. Develop evolutive architectures making use of existing genetic algorithms and study their behavior.

3. Participate to the three hackathons organized by the Extensor project in order to develop tools for evolutive neural network architecture and explainability for speaker recognition.

4. Dissemination: the research will be published in the major conferences and journals


Expected competences:

  • Phd in Machine Learning and Deep Learning
  • Experience in speech processing is positive
  • Python fluent
  • familiar with a deep learning toolkit (Pytorch, TensorFlow)

Salary : 2 600€ (after taxes)

Application contact : Anthony Larcher ( and Marie Tahon (