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Design of Application On/Off Electronic Device with Markov Model Using Speech Recognition on Android

机译：基于语音识别的马尔可夫模型应用开/关电子设备的设计

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Electronic devices are supported by a switch that is used to turn the device on and off. Manually pressed switches with distances between remote switches to cause less efficiency in saving human time and manpower. This can be solved by building a system to control electronic devices automatically. The system uses human voice commands to turn on and off electronic devices. The command will be processed into text by the Google Voice Speech Recognition library. The Android app sends human commands that have been processed by Arduino Uno R3 microcontroller. Commands are obtained after the text and data in the database are processed using the Markov Model algorithm. Communication between Android smartphone and microcontroller will be designed through a WIFI network. This system is tested based on noise level with data accuracy level with noise 0-45 dB and obtained 65% result. Based on the test response time obtained that the noise level 0-45 dB obtained results of 5.41 seconds. Based on the test results from the scenario, it can be concluded that the lower the noise generated, the better the system will also respond to commands. From the test suitability get value X = 1, meaning that the system is suitability with error rate 0. In testing accuracy to view status function get value 0 with error level 0. Testing of Markov model algorithm yields the calculated 0.125 algorithms manually and code for each command.
机译：电子设备由用于打开和关闭设备的开关支持。手动按下的开关与远程开关之间有距离，从而导致效率降低，从而节省了人力和时间。这可以通过构建一个自动控制电子设备的系统来解决。该系统使用人类语音命令来打开和关闭电子设备。该命令将由Google语音识别库处理为文本。 Android应用程序发送已由Arduino Uno R3微控制器处理过的人工命令。使用马尔可夫模型算法处理数据库中的文本和数据后，将获得命令。 Android智能手机和微控制器之间的通信将通过WIFI网络进行设计。该系统基于噪声水平进行测试，噪声水平为0-45 dB的数据精度水平，并获得65％的结果。根据测试响应时间获得的噪声水平为0-45 dB，结果为5.41秒。根据该场景的测试结果，可以得出结论，所产生的噪声越低，系统对命令的响应也就越好。根据测试的适用性，获得值X = 1，这意味着系统适用于错误率为0的情况。在测试精度中，查看状态函数的错误值为0，获得值0。马尔可夫模型算法的测试手动得出计算出的0.125算法，并为每个命令。

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