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Post-doc: Towards Affordable Self-supervised
il y a 2 semaines
**About the role**: Your role is to carry out post doc work on “affordable self-supervised learning for speech processing”. The field of speech processing has experienced an important turning point in recent years with the emergence of end-to-end neural models. These models were first trained in a supervised way, i.e. with manual annotation for each audio recording. Neural models benefit greatly from training on large corpora and therefore involve using more and more data. However, the cost of annotation quickly proved to be prohibitive. Self-supervised learning is a natural solution to this problem. There are many works in text [1,2] and image processing [3,4] that have opened the door to this kind of approach and allowed the use of more data, without human supervision. Regarding speech processing in audio, methods have also been proposed, often inspired by text and image approaches, and have brought performance gains on different speech processing tasks [5]. Nevertheless, the adoption of such approaches remains conditioned by their cost. Indeed, training requires very significant resources (e.g. 16,000 GPU hours [6]). The aim of the post-doc will be to develop approaches to limit the resources needed to train and use speech self-supervised models, to democratize their use. Furthermore, we will try to better our understanding of the mechanisms involved in the quality of self-supervised learning. Several directions can be explored: - Analyzing the behavior of the model during training - Building a more efficient architecture - Exploring training criteria - Working on the training data Technical obstacle: difficulty in implementing SSL model training. - In a context of limited resources - With "engineering tricks" that have very significant impacts (e.g. dynamic batching) - With difficulties encountered by the community to reproduce the results Scientific barriers: - Rapid evolution of the state of the art, requiring continuous scientific monitoring - A fine understanding of the methods required [1] Mikolov, T. et al. Efficient estimation of word representations in vector space. _ICLR Workshop 2013_ [2] Devlin, J. et al. Bert: Pre-training of deep bidirectional transformers for language understanding. _NAACL-HLT 2018_ [3] Grill, J. B. et al. Bootstrap your own latent-a new approach to self-supervised learning. _NeurIPS 2020_ [4] Chen, T. et al. A simple framework for contrastive learning of visual representations. _ICML 2020_ [5] Yang, S. W. et al. Superb: Speech processing universal performance benchmark. _Interspeech 2021_ [6] Baevski, A. et al. wav2vec 2.0: A framework for self-supervised learning of speech representations. _NeurIPS 2020_ **About you**: - **Skills and personal qualities** Mastery of a deep learning framework (ideally Pytorch). Mastery of the process of publishing a scientific article. Open-mindedness, curiosity, and perseverance. - ** Training** - ** Desired experiences** Knowledge of self-supervised learning methods would be a plus. Experience in complexity reduction of neural approaches would also be a plus. **Additional information**: This postdoc will allow you: - To have freedom in the choice of research directions to explore. - To contribute to a very dynamic and exciting scientific community by publishing articles and code and presenting at conferences. **Department**: Orange Innovation brings together the research and innovation activities and expertise of the Group's entities and countries. We work every day to ensure that Orange is recognized as an innovative operator by its customers and we create value for the Group and the Brand in each of our projects. With 740 researchers, thousands of marketers, developers, designers and data analysts, it is the expertise of our 6,000 employees that fuels this ambition every day. Orange Innovation anticipates technological breakthroughs and supports the Group's countries and entities in making the best technological choices to meet the needs of our consumer and business customers. **Contract**: Post Doc
