基于深度学习的语音处理实时性优化

Real-Time Optimization of Speech Processing Based on Deep Learning

针对基于深度学习的语音处理系统在实时性方面面临的挑战,提出一种连接剪枝的优化方法,旨在提高语音识别的实时性。通过深入研究基于深度学习的语音识别系统的基本原理,引入连接剪枝的方法对循环神经网络(Recurrent Neural Network,RNN)进行实时性方面的优化。采用Libri Speech数据集对优化方法和传统方法进行对比实验,结果表明优化方法能够有效提高模型的识别准确性和运行效率。

Aiming at the real-time challenge of speech processing system based on deep learning,an optimization method of connection pruning is proposed to improve the real-time performance of speech recognition.Through in-depth study of the basic principle of speech recognition system based on deep learning,the method of connecting pruning is introduced to optimize the realtime performance of Recurrent Neural Network(RNN).The LibriSpeech data set is used to compare the optimization method with the traditional method,and the results show that the optimization method can effectively improve the recognition accuracy and operation efficiency of the model.