| 苌文涵,张云翔,顾彬,相增辉,陈轩,李霁轩.结合改进DRSE-GCNN的电力调度语声识别模型*[J].,2024,43(6):1243-1249 |
| 结合改进DRSE-GCNN的电力调度语声识别模型* |
| Combining improved DRSE-GCNN for power dispatching voice recognition model |
| 投稿时间：2024-01-23 修订日期：2024-10-23 |
| 中文摘要: |
| 针对现有电力调度中语声识别方法存在的字识别错误率高和耗时长等问题，在分析语声识别技术的基础上，提出了一种改进的深度学习方法用于电力调度语声识别。将改进的深度残差收缩网络和改进的门控卷积神经网络相结合，通过改进的深度残差收缩网络提取有效特征，在通过堆叠改进的门控卷积神经网络来获取有效上下文信息。通过试验对所提方法的性能进行分析，验证其优越性。结果表明，所提方法与常规识别方法相比，在模型参数、字识别错误率和平均识别时间上均具有一定的优势，模型参数量为6.48 M，字识别错误率为2.87%，平均识别时间为0.187 s。该研究为电力调度语言识别方法的发展提供了一定的参考。 |
| 英文摘要: |
| In order to solve the problems of high error rate and long time consuming in speech recognition in power dispatching, an improved deep learning method is proposed for power dispatching language recognition based on the analysis of speech recognition technology. Combining the improved deep residual shrinkage network with the improved gated convolutional neural network, the effective features are extracted by the improved deep residual shrinkage network, and the effective contextual information is obtained improved gated convolutional neural network.The performance of the proposed method is analyzed through tests to verify its superiority.The results indicate that, compared with conventional recognitionmethods, the proposed method has certain advantages in model parameters, character recognition error rate, and average recognition time. The model parameter quantity is 6.48 M, the character recognition error rate is 2.87%, and the average recognition time is 0.187 s. This study provides a certain reference for the development of language recognition methods for power dispatch. |
| DOI：10.11684/j.issn.1000-310X.2024.06.007 |
| 中文关键词: 电力调度 语言识别 深度残差收缩网络 门控卷积神经网络 字识别错误率 |
| 英文关键词: Power dispatch Language recognition Deep residual shrinkage network Gated Convolutional Neural Network Word recognition error rate |
| 基金项目:国网科技项目(DGF5687485) |
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