Hybrid Attention-based Encoder-decoder Model for Efficient Language
  Model Adaptation
Shaoshi Ling Guoli Ye Rui Zhao Yifan Gong
Attention-based encoder-decoder (AED) speech recognition model has been
widely successful in recent years. However, the joint optimization of acoustic
model and language model in end-to-end manner has created challenges for text
adaptation. In particular, effectively, quickly and inexpensively adapting text
has become a primary concern for deploying AED systems in industry. To address
this issue, we propose a novel model, the hybrid attention-based
encoder-decoder (HAED) speech recognition model that preserves the modularity
of conventional hybrid automatic speech recognition systems. Our HAED model
separates the acoustic and language models, allowing for the use of
conventional text-based language model adaptation techniques. We demonstrate
that the proposed HAED model yields 21\% Word Error Rate (WER) improvements in
relative when out-of-domain text data is used for language model adaptation,
and with only a minor degradation in WER on a general test set compared with
conventional AED model.