Rapid Retraining on Speech Data with LSTM Recurrent Networks.

Abstract

A system that could be quickly retrained on different corpora would be of great benefit to speech recognition. Recurrent Neural Networks (RNNs) are able to transfer knowledge by simply storing and then retraining their weights. In this report, we partition the TIDIGITS database into utterances spoken by men, women, boys and girls, and successively retrain a Long Short Term Memory (LSTM) RNN on them. We find that the network rapidly adapts to new subsets of the data, and achieves greater accuracy than when trained on them from scratch. This would be useful for applications requiring either cross corpus adaptation or continually expanding datasets.