利用长短期记忆控制电器的泰语语音识别

Wuttichai Saheaw, S. Jaiyen, Anantaporn Hanskunatai
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引用次数: 1

摘要

人类语言的每个单词都具有计算机可以识别和学习的特征。在本研究中,我们提出使用深度学习模型来预测各种电器的语音开启和关闭,通过使用经过过程的声音转换方法来获得声波的值,并以不同的方式应用于训练过程。因为这个音不止一个音节,而且有类似单词的特点,很难预测。本研究以卷积神经网络(CNN)为基础,与循环神经网络(RNN)中的长短期记忆(LSTM)和泰语语音数据集的使用进行了比较,通过7种电器的开启和关闭,将音频文件前后的噪音和沉默减少了14类。实验结果表明,本文提出的长短期记忆方法具有较好的记忆准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thai Voice Recognition for Controlling Electrical appliances Using Long Short-Term Memory
Human speech possesses characteristics in each of the word that can be recognized and learned by computers. In this research, It is being proposed the use of the Deep Learning Model to predict speech turn-on and turn-off various electrical appliances, by using the sound conversion method that has been through the process to get the value of sound waves and applied toward training process in different ways. As the sound has more than 1 syllable and having characteristics of similar words that might difficult to predict. This research is based on Convolutional Neural Network (CNN) for comparison with the use of Long Short-Term Memory (LSTM), which is part of the Recurrent Neural Network (RNN) and Thai language Speech Dataset turn-on and turn-off by the 7 types of electrical appliances, the process of reducing noise and silence of the front and back of the audio files by 14 classes in total. The experimental results signify that the proposed Long Short-Term Memory can achieve the best accuracy.
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