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ABSTRACT

In recent years, artificial intelligence (AI) technologies have rapidly developed and have found a wide range of applications in many fields such as finance and economics, security surveillance, chemical engineering, medical diagnosis, and many social utilities They have made significant contributions to human life The AI techniques such as deep neural networks have attracted much attention from many researchers due to their capability to solve various complicated problems in practice In the fourth industrial revolution (a.k.a., industry 4.0), data privacy plays a crucial role for individuals, organizations, and countries In many practical scenarios, data are often under the protection of privacy restrictions It is a top priority to avoid unnecessary privacy dangers, risks, and data leakage Many security authentication techniques have been developed, including biometric techniques with biological-specific physical attributes such as fingerprints, iris patterns, voice, and molds These techniques have advantages over the conventional technique of using passwords or PINs since users do not need to memorize any codes Also, only registered users can log into the system

From the above observation, this thesis studies a speaker recognition system using machine learning techniques It mainly focuses on authenticating a speaker using a JLYHQNH\ZRUG7KHRXWSXWUHVXOWZLOOEHHLWKHU³DFFHSWDQFH´RU³UHMHFWLRQ´7KHILUVWpart of the thesis investigates the characteristics of human voice and speech Such features can be learned using different machine learning (ML) networks such as LeNet-5, ResNet, and DenseNet Note that those ML networks have been originally proposed to solve the image classification problems that are much different from the speaker verification problems Thus, this thesis presents a proper modification of those ML models such that they are more suitable to apply to the speaker verification scenarios The last part of the thesis dedicates to the hardware implementation of the proposed speaker verification system for a smart home application The project implementation uses Python programming language, the open-source platform of Tensorflow, and the embedded Raspberry Pi 3 system The experimental results are used to examine the efficiency and accuracy of the designed system

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MӨC LӨC

1+,ӊ09Ө/8Ұ19Ă17+Ҥ&6Ƭ I/Ӡ,&Ҧ0Ѫ1 II7Ï07Ҳ7 IIIABSTRACT IV/Ӡ,&$0Ĉ2$1&Ӫ$7È& *,Ҧ V0Ө&/Ө& VI'$1+0Ө&%Ҧ1*%,ӆ8 IXDA1+0Ө&+Î1+Ҧ1+ X'$1+0Ө&&È&7Ӯ9,ӂ7 7Ҳ7 XIII

2.6.3 Backpropagation and Derivatives 29

2.7Convolution Neural Network 29

2.7.1 Convolutional Layer 30

2.7.2 /ӟSNtFKKRҥWSKLWX\ӃQ5H/8 31

2.7.3 Pooling layer 31

2.7.4 Fully connected layer 31

2.7.5 Batch norm layer 31

ix

DANH MӨC BҦNG BIӆU

Bҧng 2-1 Tóm tҳt các công trình nghiên cӭu vӅ nhұn dҥQJQJѭӡi nói [6] 11

Bҧng 2-2 Tәng sӕ OѭӧQJÿһc tUѭQJWKXÿѭӧc tӯ mӝt vector cӫDQJѭӡi nói chuҭn 18

Bҧng 2-3 Bҧng thӇ hiӋQÿӝ lӟn cӫa các loҥi âm thanh thӵc tӃ 33

Bҧng 5-Ĉӝ chính xác cӫa các mô hình nhұn dҥQJQJѭӡi nói phө thuӝc tӯ khóa ´[LQFKjREҥn có khӓHNK{QJ"´ 68

Bҧng 5-Ĉӝ chính xác cӫa các hӋ thӕng nhұn dҥQJQJѭӡi nói phө thuӝc tӯ khóa sӱ dөng tұp dӳ liӋu XiaoDu [29] 68

Bҧng 5-3 Các chӍ sӕ ÿiQKJLiÿӝ chính xác 69

Bҧng 5-4 Các chӍ sӕ ÿiQKJLiÿӝ chính xác 70

Bҧng 5-5 KӃt quҧ kiӇm thӱ thӵc tӃ vӟLP{KuQK'HQVH1HWÿmFKӍnh sӱa 71

Bҧng 5-6 KӃt quҧ kiӇm thӱ thӵc tӃ vӟi mô hình neural sâu 72

DANH MӨC HÌNH ҦNH

Hình 1-1 Dӵ ÿRiQJLiWUӏ thӏ WUѭӡng cӫa các ngành AI toàn cҫu tӯ QăP-2025

1

Hình 1-2 TӍ lӋ các hoҥWÿӝng kinh doanh sӱ dөng các dӏch vө ÿLӋn tӱ khҧo sát 2

Hình 2-1 Tәng quan vӅ nhұn dҥQJQJѭӡi nói [6] 9

Hình 2-&iFÿһc tính cӫa hӋ thӕng nhұn dҥQJQJѭӡi nói [6] 10

Hình 2-&iFEѭӟc MFCC 14

Hình 2-³;LQFKjREҥn có khӓHNK{QJ´WUѭӟc và sau khi pre-emphasis 14

Hình 2-5 Cӱa sә hamming 15

Hình 2-6 Tín hiӋu gӕc và tín hiӋXVDXNKLÿѭӧc cӱa sӕ hóa 16

Hình 2-7 Giá trӏ DFT cӫa tín hiӋXVDXÿѭӧc cӱa sә hóa hình 2-6 16

Hình 2-8 Bӝ lӑc Mel tam giác vӟi 10 bӝ lӑc cho hӋ thӕng lҩy mүu tҥi 48000 Hz 17

Hình 2-14 Mҥng neural không sâu và mҥng neural sâu 27

Hình 2-15 Quá trình lan truyӅn trong mҥng neural 28

xi

Hình 3-7 Khӕi nӕi tҳt trong mҥng [19] 40

Hình 3-8 KiӃn trúc mҥng ResNet-50 [20] 40

Hình 3-9 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi batch szie = 100 41

Hình 3-10 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi batch szie = 20 41

Hình 3-11 Khӕi Inception-v1 [22] 42

Hình 3-12 Khӕi Inception-v2 [23] 43

Hình 3-13 KiӃn trúc mҥng Inception-v4 [21] 43

Hình 3-14 KӃt quҧ huҩn luyӋn và kiӇm thӱ Incpetion-v4 44

Hình 3-15 Khӕi initial cӫa mҥng Enet [24] 44

Hình 3-16 Khӕi nút cә chai cӫa mҥng Enet [24] 45

Hình 3-17 KiӃn trúc mҥng Enet [24] 45

Hình 3-18 KӃt quҧ huҩn luyӋn và kiӇm tӱ ENet 46

Hình 3-19 Ví dө vӅ tích chұp theo chiӅu sâu [26] 47

Hình 3-20 Khӕi tích chұp chuҭn và tích chұp theo chiӅu sâu trong MobileNets [25] 47

Hình 3-21 KӃt quҧ huҩn luyӋn và kiӇm thӱ 0RELOH1HWO~Fÿҫu 48

Hình 3-22 KӃt quҧ huҩn luyӋn và kiӇm thӱ MobileNet sau khi chӍnh sӱa 48

Hình 3-23 KiӃn trúc mҥng DenseNet [27] 49

Hình 3-24 KiӃn trúc mҥng DenseNet vӟi 3 khӕi Dense [27] 49

Hình 3-25 KӃt quҧ huҩn luyӋn và kiӇm thӱ DenseNet 50

Hình 3-26 Mô hình DensNet khi trích xuҩt Embedded Vector 51

Hình 3-Ĉӝ WѭѫQJTXDQFӫa tұp kiӇm thӱ 51

Hình 3-28 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟL'HQVH1HWVDXNKLWăQJFѭӡng dӳ liӋu 52

Hình 3-Ĉӝ WѭѫQJTXDQFӫa tұp kiӇm thӱ VDXNKLWăQJFѭӡng dӳ liӋu 52

Hình 3-30 Mô hình DenseNet sau khi sӱDÿәi 53

Hình 3-31 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟLP{KuQKÿmVӱDÿәi 53

Hình 3-32 Giá trӏ WѭѫQJTXDQFӫa 2 loҥLQJѭӡi dùng sau khi thêm vào các lӟp Dense 54

Hình 3-33 Mô hình mҥng neural netwwork thiӃt kӃ cho bài toán 55

Hình 3-34 KӃt quҧ huҩn luyӋn và kiӇm thӱ cӫa neural network 56

Hình 3-35 giá trӏ WѭѫQJTXDQFӫa 2 loҥLQJѭӡi dùng vӟi neural network 56

Hình 4-1 Graph trong TensorFlow 59

Hình 4-2 Raspberry Pi 3 60

Hình 4-3 ThiӃt bӏ card âm thanh kӃt nӕi vӟi Raspberry Pi 3 61

Hình 5-1 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi mҥng LeNet-5 65

Hình 5-2 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi mҥng ResNet 65

Hình 5-3 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi mҥng DenseNet 66

Hình 5-4 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi mҥng inception-v4 66

Hình 5-5 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi mҥng ENet 67

Hình 5-6 KӃt quҧ huҩn luyӋn và kiӇm thӱ vӟi mҥng MobileNet 67

Hình 5-7 Confusion matrix cӫa kӃt quҧ mô phӓng 69

Hình 5-8 KӃt quҧ ÿiQKJLi[iFWKӵc cӫa mô hình 69

Hình 5-9 Confusion matrix cӫa kӃt quҧ mô phӓng 70

Hình 5-10 KӃt quҧ ÿiQKJLi[iFWKӵc cӫa mô hình 70

Hình 5-11 HӋ thӕQJNKLÿѭDYjRWKӵc tӃ kiӇm thӱ 70

Hình 5-12 KӃt quҧ khҧo sát vӟLFiFFODVVFKѭDÿăQJNê 72

xiii

DANH MӨC CÁC TӮ VIӂT TҲT

FDLP Frequency Domain Linear Prediction 'ӵÿRiQWX\ӃQWtQKPLӅQWҫQVӕ

Gammatone

UBM

GMM-Gaussian Mixture Model-Universal Background Model

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IDFT Inverse Discrete Fourier transform %LӃQÿәL)RXULHUUӡLUҥFQJѭӧF

MFCC Mel Frequency Cepstral Coefficient +ӋVӕFHSVWUDOFӫDEӝOӑF0HO

1 &+ѬѪ1*3+ҪN MӢ ĈҪU

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1.1 Ĉһt vҩQÿӅ nghiên cӭu

1.1.1 ;XKѭӟQJWăQJWUѭӣng cӫDFiFOƭQKYӵc trí thông minh nhân tҥo

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2

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1.2 Lý do chӑQÿӅ tài

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