  Building an automatic speech recognition (ASR) system from scratch requires a
large amount of annotated speech data, which is difficult to collect in many
languages. However, there are cases where the low-resource language shares a
common acoustic space with a high-resource language having enough annotated
data to build an ASR. In such cases, we show that the domain-independent
acoustic models learned from the high-resource language through unsupervised
domain adaptation (UDA) schemes can enhance the performance of the ASR in the
low-resource language. We use the specific example of Hindi in the source
domain and Sanskrit in the target domain. We explore two architectures: i)
domain adversarial training using gradient reversal layer (GRL) and ii) domain
separation networks (DSN). The GRL and DSN architectures give absolute
improvements of 6.71% and 7.32%, respectively, in word error rate over the
baseline deep neural network model when trained on just 5.5 hours of data in
the target domain. We also show that choosing a proper language (Telugu) in the
source domain can bring further improvement. The results suggest that UDA
schemes can be helpful in the development of ASR systems for low-resource
languages, mitigating the hassle of collecting large amounts of annotated
speech data.
