The goal of the ALLIES project is to encourage and demonstrate the development of autonomous systems, able to sustain performance across time according to a given learning scenario. A learning scenario defines the importance given to the performance on “past” and “present” data in the optimization (or evaluation) process. By setting the learning scenario, a human supervisor (HS) allows or forbids the system to forget. In ALLIES, an autonomous system is fully unsupervised, incrementally adapting its models but also their structure to learn or forget events according to the given learning scenario and data that it automatically collects across time. Current unsupervised systems optimize their performance for a single set of data. After ALLIES, autonomous systems will optimize their performance across time by considering together past and present data.

• 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
• IberSpeech 2021: Active correction for speaker diarization with human in the loop, Yevhenii Prokopalo, Meysam Shamsi, Loïc Barrault, Sylvain Meignier, Anthony Larcher