Incorporating Class-based Language Model for Named Entity Recognition in
  Factorized Neural Transducer
Peng Wang Yifan Yang Zheng Liang Tian Tan Shiliang Zhang Xie Chen
In spite of the excellent strides made by end-to-end (E2E) models in speech
recognition in recent years, named entity recognition is still challenging but
critical for semantic understanding. In order to enhance the ability to
recognize named entities in E2E models, previous studies mainly focus on
various rule-based or attention-based contextual biasing algorithms. However,
their performance might be sensitive to the biasing weight or degraded by
excessive attention to the named entity list, along with a risk of false
triggering. Inspired by the success of the class-based language model (LM) in
named entity recognition in conventional hybrid systems and the effective
decoupling of acoustic and linguistic information in the factorized neural
Transducer (FNT), we propose a novel E2E model to incorporate class-based LMs
into FNT, which is referred as C-FNT. In C-FNT, the language model score of
named entities can be associated with the name class instead of its surface
form. The experimental results show that our proposed C-FNT presents
significant error reduction in named entities without hurting performance in
general word recognition.