Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Light-weight contextual spelling correction model for customizing
  transducer-based speech recognition systems   It's challenging to customize transducer-based automatic speech recognition
(ASR) system with context information which is dynamic and unavailable during
model training. In this work, we introduce a light-weight contextual spelling
correction model to correct context-related recognition errors in
transducer-based ASR systems. We incorporate the context information into the
spelling correction model with a shared context encoder and use a filtering
algorithm to handle large-size context lists. Experiments show that the model
improves baseline ASR model performance with about 50% relative word error rate
reduction, which also significantly outperforms the baseline method such as
contextual LM biasing. The model also shows excellent performance for
out-of-vocabulary terms not seen during training.

16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc