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Learning to Adapt: A Meta-learning Approach for Speaker Adaptation
The performance of automatic speech recognition systems can be improved by adapting an acoustic model to compensate for the mismatch between training and testing conditions, for example by adapting to unseen speakers. The success of speaker adaptation methods relies on selecting weights that are suitable for adaptation and using good adaptation schedules to update these weights in order not to overfit to the adaptation data. In this paper we investigate a principled way of adapting all thedoi:10.21437/interspeech.2018-1244 dblp:conf/interspeech/KlejchF018 fatcat:x3bfiz3t2fcatap52bl4cirsz4