Automatic Correction of Speech Recognized Mathematical Equations using Encoder-Decoder Attention Model

被引：1
|
作者：
Mounika, Y. [1 ]
Tarakaram, Y. [1 ]
Prasanna, Y. Lakshmi [1 ]
Gupta, Deepa [1 ]
Pati, Peeta Basa [1 ]
机构：
[1] Amrita Vishwa Vidyapeetham, Amrita Sch Comp, Dept Comp Sci & Engn, Bangalore, Karnataka, India
来源：
2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON | 2022年
关键词：
Phonetic errors; Automatic Speech Recognition (ASR); Mathematical equations; Encoder-Decoder; Attention; Word Error Rate (WER); Character Error Rate (CER);
D O I：
10.1109/INDICON56171.2022.10039926
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Automated correction of common and systematic errors caused by the ASR system is part of the post-processing of automatic speech recognition (ASR). The ASR system's output is prone to grammatical, spelling and phonetic problems. The sentences which are the outputs from Automated Speech Recognition (ASR) models have been refined using error correction approaches to obtain a reduced word error rate (WER) and Character Error Rate (CER) than the initial ASR outputs. In this paper we propose a model that reduces the word error rate and character error rate of the speech recognized mathematical equations. The proposed model is the encoder-decoder with attention model.
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页数：6
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