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Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
Wang, Xiaoqiang
;
Liu, Yanqing
;
Li, Jinyu
;
Miljanic, Veljko
;
Zhao, Sheng
;
Khalil, Hosam
. - : arXiv, 2022
Abstract:
Contextual biasing is an important and challenging task for end-to-end automatic speech recognition (ASR) systems, which aims to achieve better recognition performance by biasing the ASR system to particular context phrases such as person names, music list, proper nouns, etc. Existing methods mainly include contextual LM biasing and adding bias encoder into end-to-end ASR models. In this work, we introduce a novel approach to do contextual biasing by adding a contextual spelling correction model on top of the end-to-end ASR system. We incorporate contextual information into a sequence-to-sequence spelling correction model with a shared context encoder. Our proposed model includes two different mechanisms: autoregressive (AR) and non-autoregressive (NAR). We propose filtering algorithms to handle large-size context lists, and performance balancing mechanisms to control the biasing degree of the model. We demonstrate the proposed model is a general biasing solution which is domain-insensitive and can be ... : This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible ...
Keyword:
Audio and Speech Processing eess.AS
;
Computation and Language cs.CL
;
FOS Computer and information sciences
;
FOS Electrical engineering, electronic engineering, information engineering
;
Sound cs.SD
URL:
https://dx.doi.org/10.48550/arxiv.2203.00888
https://arxiv.org/abs/2203.00888
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