喚醒詞系統之嵌入式系統實現;Embedded System Implementation of Wake-Up Word Recognition

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77749

Title:	喚醒詞系統之嵌入式系統實現;Embedded System Implementation of Wake-Up Word Recognition
Authors:	韓多諾;Handono, Maystya Tri
Contributors:	資訊工程學系
Keywords:	唤醒一词;卷積神經網絡;wake-up word;convolution neural network;tensorflow
Date:	2018-08-17
Issue Date:	2018-08-31 14:54:52 (UTC+8)
Publisher:	國立中央大學
Abstract:	喚醒字系統用於將智能設備置於警報狀態，以便它期望進一步的口頭命令。喚醒字系統允許智能電話，汽車多媒體系統或家庭自動化系統等設備的免提操作。在本論文中，我們使用 deep learning 架構實現喚醒字系統。該系統使用Tensorflow框架進行培訓。在測試中，我們在沒有Tensorflow框架的情況下實現推理。原因是給一些不支持Tensorflow的嵌入式系統設備。將使用虛警率（FAR），錯誤拒絕率（FRR）和準確度來評估喚醒字系統。基線具有95％的準確度，0.09％的FAR和0.02％的FRR。 Tensorflow框架的測試結果具有0.03％FAR，0.07％FRR，精度為96％。沒有張量流的測試推斷得到0.06％FAR，0.11％FRR，精度為95.8％。兩種實現之間的差異大約為 3％。 ;Wake-up word system is used to put an intelligent device in a state of alert so that it expects further spoken commands. The wake-up word system allows for hands-free operation of devices such as smart phones, multimedia systems in cars or home automation system. In this thesis, we implement the wake-up word system using deep learning architecture. The system is implemented using Tensorflow framework for training. In the testing, we implements the inference without Tensorflow framework. The reason is to give some embedded system device that has not support with Tensorflow. The wake-up word system will be evaluated using False Alarm Rate (FAR), False Rejection Rate (FRR), and the accuracy. The baseline has 95% of accuracy, 0.09% of FAR, and 0.02% of FRR. The testing result with Tensorflow framework has 0.03% FAR, 0.07% FRR, and the accuracy is 96%. The testing inference without tensorflow resulting 0.06% FAR, 0.11% FRR and the accuracy is 95.8%. The different between the two implementation is around 3%.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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