  We propose a system to develop a basic automatic speech recognizer(ASR) for
Cantonese, a low-resource language, through transfer learning of Mandarin, a
high-resource language. We take a time-delayed neural network trained on
Mandarin, and perform weight transfer of several layers to a newly initialized
model for Cantonese. We experiment with the number of layers transferred, their
learning rates, and pretraining i-vectors. Key findings are that this approach
allows for quicker training time with less data. We find that for every epoch,
log-probability is smaller for transfer learning models compared to a
Cantonese-only model. The transfer learning models show slight improvement in
CER.
