Automated Speech Recognition System for Dispatching Call Recordings in The Underground Coal Mines
Authors: 
Guoyuan Lin, Lei Zhao, Jueting Liu, Zemeng Liu, Minda Yao, Wei Chen, Yingchun Liu, Zehua Wang, Hengbo LiAuthors Info & Claims
Pages 363 - 367
Abstract
In this paper, we proposed an automated speech recognition system focus on the dispatching call recordings in the underground coal mines which promoted the development of intelligent coal mining. The main challenges of the speech recognition system are the noise of recordings and the dialect speech. We employed a voice activity detection module to preprocess the recordings, this module is able to reduce the noise and segment the long recording speech; then the Conformer model with CTC algorithm is utilized to train the ASR module. To get better performance, the WenetSpeech pretrained model is embedded for fine-tuning. The result shows that compared with the other general speech recognition systems, our ASR system has great advance in recognizing the dispatching call recordings of Huaibei dialect.
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Index Terms
- Automated Speech Recognition System for Dispatching Call Recordings in The Underground Coal Mines
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Published In
July 2023
383 pages
ISBN:9798400707575
DOI:10.1145/3614008
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 17 October 2023
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- Fundamental Research Funds for the Central Universities
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SPML 2023
SPML 2023: 2023 6th International Conference on Signal Processing and Machine Learning
July 14 - 16, 2023
Tianjin, China
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