End-To-end Evolutive Neural Networks for Speaker Recognition (EXTENSOR)
ExTENSoR proposes fundamental research that aims to explore the potential of using end-to-end and automatically learned / evolving articial neural networks in order to overcome the limitations of hand-crafted features and network topologies that characterise the current state of the art in many fields of speech processing. ExTENSoR also aims to bring new insights to what information in speech signals is being used in order to arrive at the scores or decisions produced by the network. ExTENSoR will pursue its objectives within the context of automatic speaker recognition and anti-spoofing, two fields of speech processing research showing burgeoning interest in end-to-end, evolutive learning.
- ICASSP 2021: Speaker Embedding for Diarization of Broadcast data in the ALLIES Challenge, Anthony Larcher, Ambuj Mehrish, Marie Tahon, Sylvain Meignier, Jean Carrive, David Doukhan, Olivier Galibert, Nicholas Evans
- ICASSP 2021: END-TO-END ANTI-SPOOFING WITH RAWNET2, Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans and Anthony Larcher