基于深度神经网络的语音质量评价方法研究

Teng Haikun, W. Shiying, Li Lunbin
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摘要

随着人工智能研究和高性能计算机、深度学习及相关理论的不断发展,神经网络已成为主流声学模型。本文从语音识别的总体框架和基本原理入手,研究了深度神经网络下的语言识别系统。通过深度神经网络,可以更好地处理大量的中文数据处理过程,降低模型复杂度,提高中文语音识别率的准确率。为此,本文以基于深度神经网络的汉语语音识别系统为研究对象,研究识别后的语音质量。结果表明,深度神经网络语言模型具有较好的识别精度。
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Research on Speech Quality Evaluation Method Based on Deep Neural Network
With the continuous development of artificial intelligence research and high-performance computers, deep learning and related theories, neural networks have become mainstream acoustic models. This article starts with the overall framework and basic principles of speech recognition, and studies the language recognition system under deep neural network. Through deep neural network, it can better deal with the large amount of Chinese data processing process, reduce model complexity, and improve the accuracy of Chinese speech recognition rate. For this reason, this paper uses the deep neural network-based Chinese speech recognition system as the research object to study the speech quality after recognition. The results verify that the deep neural network language model has a better recognition accuracy.
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