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Artificial Intelligence Technique for Speech Recognition based on Neural Networks

机译:基于神经网络的人工智能语音识别技术

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摘要

Creation of natural human sources to communicate with the computer is currently one of the greatest challenges of modern science. The speech input facility is the most user-friendly way, adopted by development of speech recognition based on sophisticated technologies. Scientists began the selection of informative signs, describing the voice signal, afterwards the task of classification of speech signals as a set of informative signs. The development of methods of signal processing in the absence of sufficient models lead to questions about the processes of generation signals using artificial neural networks, As a resu when building a signal processing system, the structure of the network should be selected; according to parameters of the signals and training network using an algorithm to maximize the use of the information contained in the data of the experiment. This article proposes the Application of wavelet transform for reduction of the value of artificial neural networks for speech recognition tasks this method a present Study a new modification of neural networks a neural network with inverse Wavelet Decomposition of the signal. For example, the speech recognition task, the analysis of the proposed method. The effectiveness of the method is proved by the results of computer simulations.
机译:创建与计算机通信的自然人力资源是当前现代科学的最大挑战之一。语音输入功能是最用户友好的方式,被基于复杂技术的语音识别开发所采用。科学家开始选择用于描述语音信号的信息性标志,然后将语音信号分类为一组信息性标志。在缺乏足够模型的情况下信号处理方法的发展引起了关于使用人工神经网络生成信号的过程的疑问。建立信号处理系统时,应选择网络结构;根据信号和训练网络的参数,使用一种算法来最大程度地利用实验数据中包含的信息。本文提出了小波变换在减少人工神经网络用于语音识别任务中的价值中的应用。该方法是目前研究神经网络的一种新的改进,即具有逆小波分解信号的神经网络。例如语音识别任务,分析所提出的方法。计算机仿真结果证明了该方法的有效性。

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