基于卷积神经网络的实时语音分割优化研究

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中文摘要：为进一步提高实时语音分割的性能,分析基于卷积神经网络(Convolutional Neural Networks,CNN)的实时语音分割优化方法.首先,介绍CNN的基本结构和在语音分割中的数学原理.其次,引入修剪技术,根据权重的重要性分数决定保留或删除权重.实验结果表明,该方法的准确率、召回率、F1值及用时均优于传统CNN.

外文标题：Research on Real time Speech Segmentation Optimization Based on Convolutional Neural Networks

外文摘要：To further improve the performance of real-time speech segmentation, analyze the optimization method of real-time speech segmentation based on Convolutional Neural Network (CNN). Firstly, introduce the basic structure of CNN and its mathematical principles in speech segmentation. Secondly, introduce pruning techniques to determine whether to retain or delete weights based on their importance scores. The experimental results show that the accuracy, recall, F1 value, and time consumption of this method are all better than traditional CNN.

外文关键词：

Convolutional Neural Network (CNN)speech segmentationreal time performancepruning technique

作者：

杨波

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作者单位：

陇南师范高等专科学校数信学院,甘肃陇南 742500

关键词：

卷积神经网络(CNN) 语音分割实时性修剪技术

出版年：

2024

DOI：