Modular Domain Adaptation for Conformer-Based Streaming ASR
Qiujia Li Bo Li Dongseong Hwang Tara N. Sainath Pedro M. Mengibar
Speech data from different domains has distinct acoustic and linguistic
characteristics. It is common to train a single multidomain model such as a
Conformer transducer for speech recognition on a mixture of data from all
domains. However, changing data in one domain or adding a new domain would
require the multidomain model to be retrained. To this end, we propose a
framework called modular domain adaptation (MDA) that enables a single model to
process multidomain data while keeping all parameters domain-specific, i.e.,
each parameter is only trained by data from one domain. On a streaming
Conformer transducer trained only on video caption data, experimental results
show that an MDA-based model can reach similar performance as the multidomain
model on other domains such as voice search and dictation by adding per-domain
adapters and per-domain feed-forward networks in the Conformer encoder.