Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
TABLE IX: WER(%) on AMI–IHM and one-shot adaptation
['Model', 'dev', 'eval']
One-shot adaptation: By one-shot adaptation we mean the scenario in which LHUC transforms were estimated once for a held-out speaker and then used many times in a single pass system for this speaker. We performed those experiments on AMI IHM data, and report results on dev and eval which contain 21 and 16 unique speakers taking part in 18 and 16 different meetings, respectively. Each speaker participates in multiple meetings: to some degree, adapting to a speaker in one meeting, then applying the adaptation transform to the same speaker in the other meetings simulates a real-life condition where it is possible to assume the speaker identity without having to perform speaker diarisation (e.g. personal devices).