Detecting and recovering out-of-vocabulary (OOV) words is always challenging
for Automatic Speech Recognition (ASR) systems. Many existing methods focus on
modeling OOV words by modifying acoustic and language models and integrating
context words cleverly into models. To train such complex models, we need a
large amount of data with context words, additional training time, and
increased model size. However, after getting the ASR transcription to recover
context-based OOV words, the post-processing method has not been explored much.
In this work, we propose a post-processing technique to improve the performance
of context-based OOV recovery. We created an acoustically boosted language
model with a sub-graph made at phone level with an OOV words list. We proposed
two methods to determine a suitable cost function to retrieve the OOV words
based on the context. The cost function is defined based on phonetic and
acoustic knowledge for matching and recovering the correct context words in the
decode. The effectiveness of the proposed cost function is evaluated at both
word-level and sentence-level. The evaluation results show that this approach
can recover an average of 50% context-based OOV words across multiple
categories.

本文提出了一种基于声学增强的语言模型的后处理方法，通过基于语境和音素知识来匹配和恢复正确的上下文单词，该方法可以在多个类别中恢复平均 50% 的基于上下文的未登录词。