Affiliations: [a] School of Foreign Languages, Guangdong Ocean University Cunjin College, Zhanjiang, Guangdong, China
| [b] Department of Economics, Sejong University, Seoul, South Korea
Correspondence:
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Corresponding author. Deng Bowen, School of Foreign Languages, Guangdong Ocean University Cunjin College, Zhanjiang, Guangdong, China. E-mail: [email protected].
Abstract: The performance of the speech recognition system for English classroom teaching is largely affected by the surrounding environment. These interference signals will seriously reduce the quality and intelligibility of the speech signal, thereby greatly reducing the performance of the far-field speech recognition system. Aiming at word order detection in English classroom teaching, this paper proposes an analysis model based on block coding and improved genetic algorithm. Moreover, for DNN-based single-channel speech enhancement algorithms, this paper proposes PDNNs and PLSTMs to solve the problem of serious performance degradation of prototype DNN speech enhancement under low signal-to-noise ratio. This method decomposes the entire enhancement task into multiple subtasks to complete, and the previously completed subtasks will provide prior knowledge for the subsequent subtasks, so that the subsequent subtasks can learn its goals better. In general, the experimental results prove the reliability of the model constructed in this paper.
Keywords: Block coding, genetic algorithm, machine learning, word order detection
