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๊ฐ์ธํ ์์ฑ์ธ์์ ์ํ DNN ๊ธฐ๋ฐ ์ํฅ ๋ชจ๋ธ๋ง
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ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2019. 2. ๊น๋จ์.๋ณธ ๋
ผ๋ฌธ์์๋ ๊ฐ์ธํ ์์ฑ์ธ์์ ์ํด์ DNN์ ํ์ฉํ ์ํฅ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ๋ค์ ์ ์ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ํฌ๊ฒ ์ธ ๊ฐ์ง์ DNN ๊ธฐ๋ฐ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ฒซ ๋ฒ์งธ๋ DNN์ด ๊ฐ์ง๊ณ ์๋ ์ก์ ํ๊ฒฝ์ ๋ํ ๊ฐ์ธํจ์ ๋ณด์กฐ ํน์ง ๋ฒกํฐ๋ค์ ํตํ์ฌ ์ต๋๋ก ํ์ฉํ๋ ์ํฅ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ์ด๋ค. ์ด๋ฌํ ๊ธฐ๋ฒ์ ํตํ์ฌ DNN์ ์๊ณก๋ ์์ฑ, ๊นจ๋ํ ์์ฑ, ์ก์ ์ถ์ ์น, ๊ทธ๋ฆฌ๊ณ ์์ ํ๊ฒ๊ณผ์ ๋ณต์กํ ๊ด๊ณ๋ฅผ ๋ณด๋ค ์ํํ๊ฒ ํ์ตํ๊ฒ ๋๋ค. ๋ณธ ๊ธฐ๋ฒ์ Aurora-5 DB ์์ ๊ธฐ์กด์ ๋ณด์กฐ ์ก์ ํน์ง ๋ฒกํฐ๋ฅผ ํ์ฉํ ๋ชจ๋ธ ์ ์ ๊ธฐ๋ฒ์ธ ์ก์ ์ธ์ง ํ์ต (noise-aware training, NAT) ๊ธฐ๋ฒ์ ํฌ๊ฒ ๋ฐ์ด๋๋ ์ฑ๋ฅ์ ๋ณด์๋ค.
๋ ๋ฒ์งธ๋ DNN์ ํ์ฉํ ๋ค ์ฑ๋ ํน์ง ํฅ์ ๊ธฐ๋ฒ์ด๋ค. ๊ธฐ์กด์ ๋ค ์ฑ๋ ์๋๋ฆฌ์ค์์๋ ์ ํต์ ์ธ ์ ํธ ์ฒ๋ฆฌ ๊ธฐ๋ฒ์ธ ๋นํฌ๋ฐ ๊ธฐ๋ฒ์ ํตํ์ฌ ํฅ์๋ ๋จ์ผ ์์ค ์์ฑ ์ ํธ๋ฅผ ์ถ์ถํ๊ณ ๊ทธ๋ฅผ ํตํ์ฌ ์์ฑ์ธ์์ ์ํํ๋ค. ์ฐ๋ฆฌ๋ ๊ธฐ์กด์ ๋นํฌ๋ฐ ์ค์์ ๊ฐ์ฅ ๊ธฐ๋ณธ์ ๊ธฐ๋ฒ ์ค ํ๋์ธ delay-and-sum (DS) ๋นํฌ๋ฐ ๊ธฐ๋ฒ๊ณผ DNN์ ๊ฒฐํฉํ ๋ค ์ฑ๋ ํน์ง ํฅ์ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ ์ํ๋ DNN์ ์ค๊ฐ ๋จ๊ณ ํน์ง ๋ฒกํฐ๋ฅผ ํ์ฉํ ๊ณต๋ ํ์ต ๊ธฐ๋ฒ์ ํตํ์ฌ ์๊ณก๋ ๋ค ์ฑ๋ ์
๋ ฅ ์์ฑ ์ ํธ๋ค๊ณผ ๊นจ๋ํ ์์ฑ ์ ํธ์์ ๊ด๊ณ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ํํํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ multichannel wall street journal audio visual (MC-WSJAV) corpus์์์ ์คํ์ ํตํ์ฌ, ๊ธฐ์กด์ ๋ค์ฑ๋ ํฅ์ ๊ธฐ๋ฒ๋ค๋ณด๋ค ๋ฐ์ด๋ ์ฑ๋ฅ์ ๋ณด์์ ํ์ธํ์๋ค.
๋ง์ง๋ง์ผ๋ก, ๋ถํ์ ์ฑ ์ธ์ง ํ์ต (Uncertainty-aware training, UAT) ๊ธฐ๋ฒ์ด๋ค. ์์์ ์๊ฐ๋ ๊ธฐ๋ฒ๋ค์ ํฌํจํ์ฌ ๊ฐ์ธํ ์์ฑ์ธ์์ ์ํ ๊ธฐ์กด์ DNN ๊ธฐ๋ฐ ๊ธฐ๋ฒ๋ค์ ๊ฐ๊ฐ์ ๋คํธ์ํฌ์ ํ๊ฒ์ ์ถ์ ํ๋๋ฐ ์์ด์ ๊ฒฐ์ ๋ก ์ ์ธ ์ถ์ ๋ฐฉ์์ ์ฌ์ฉํ๋ค. ์ด๋ ์ถ์ ์น์ ๋ถํ์ ์ฑ ๋ฌธ์ ํน์ ์ ๋ขฐ๋ ๋ฌธ์ ๋ฅผ ์ผ๊ธฐํ๋ค. ์ด๋ฌํ ๋ฌธ์ ์ ์ ๊ทน๋ณตํ๊ธฐ ์ํ์ฌ ์ ์ํ๋ UAT ๊ธฐ๋ฒ์ ํ๋ฅ ๋ก ์ ์ธ ๋ณํ ์ถ์ ์ ํ์ตํ๊ณ ์ํํ ์ ์๋ ๋ด๋ด ๋คํธ์ํฌ ๋ชจ๋ธ์ธ ๋ณํ ์คํ ์ธ์ฝ๋ (variational autoencoder, VAE) ๋ชจ๋ธ์ ์ฌ์ฉํ๋ค. UAT๋ ์๊ณก๋ ์์ฑ ํน์ง ๋ฒกํฐ์ ์์ ํ๊ฒ๊ณผ์ ๊ด๊ณ๋ฅผ ๋งค๊ฐํ๋ ๊ฐ์ธํ ์๋ ๋ณ์๋ฅผ ๊นจ๋ํ ์์ฑ ํน์ง ๋ฒกํฐ ์ถ์ ์น์ ๋ถํฌ ์ ๋ณด๋ฅผ ์ด์ฉํ์ฌ ๋ชจ๋ธ๋งํ๋ค. UAT์ ์๋ ๋ณ์๋ค์ ๋ฅ ๋ฌ๋ ๊ธฐ๋ฐ ์ํฅ ๋ชจ๋ธ์ ์ต์ ํ๋ uncertainty decoding (UD) ํ๋ ์์ํฌ๋ก๋ถํฐ ์ ๋๋ ์ต๋ ์ฐ๋ ๊ธฐ์ค์ ๋ฐ๋ผ์ ํ์ต๋๋ค. ์ ์๋ ๊ธฐ๋ฒ์ Aurora-4 DB์ CHiME-4 DB์์ ๊ธฐ์กด์ DNN ๊ธฐ๋ฐ ๊ธฐ๋ฒ๋ค์ ํฌ๊ฒ ๋ฐ์ด๋๋ ์ฑ๋ฅ์ ๋ณด์๋ค.In this thesis, we propose three acoustic modeling techniques for robust automatic speech recognition (ASR). Firstly, we propose a DNN-based acoustic modeling technique which makes the best use of the inherent noise-robustness of DNN is proposed. By applying this technique, the DNN can automatically learn the complicated relationship among the noisy, clean speech and noise estimate to phonetic target smoothly. The proposed method outperformed noise-aware training (NAT), i.e., the conventional auxiliary-feature-based model adaptation technique in Aurora-5 DB.
The second method is multi-channel feature enhancement technique. In the general multi-channel speech recognition scenario, the enhanced single speech signal source is extracted from the multiple inputs using beamforming, i.e., the conventional signal-processing-based technique and the speech recognition process is performed by feeding that source into the acoustic model. We propose the multi-channel feature enhancement DNN algorithm by properly combining the delay-and-sum (DS) beamformer, which is one of the conventional beamforming techniques and DNN. Through the experiments using multichannel wall street journal audio visual (MC-WSJ-AV) corpus, it has been shown that the proposed method outperformed the conventional multi-channel feature enhancement techniques.
Finally, uncertainty-aware training (UAT) technique is proposed. The most of the existing DNN-based techniques including the techniques introduced above, aim to optimize the point estimates of the targets (e.g., clean features, and acoustic model parameters). This tampers with the reliability of the estimates. In order to overcome this issue, UAT employs a modified structure of variational autoencoder (VAE), a neural network model which learns and performs stochastic variational inference (VIF). UAT models the robust latent variables which intervene the mapping between the noisy observed features and the phonetic target using the distributive information of the clean feature estimates. The proposed technique outperforms the conventional DNN-based techniques on Aurora-4 and CHiME-4 databases.Abstract i
Contents iv
List of Figures ix
List of Tables xiii
1 Introduction 1
2 Background 9
2.1 Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Experimental Database . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Aurora-4 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Aurora-5 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.3 MC-WSJ-AV DB . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.4 CHiME-4 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Two-stage Noise-aware Training for Environment-robust Speech
Recognition 25
iii
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Noise-aware Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Two-stage NAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.1 Lower DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.2 Upper DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.3 Joint Training . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.1 GMM-HMM System . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.2 Training and Structures of DNN-based Techniques . . . . . . 37
3.4.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 40
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4 DNN-based Feature Enhancement for Robust Multichannel Speech
Recognition 45
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Observation Model in Multi-Channel Reverberant Noisy Environment 49
4.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3.1 Lower DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.3.2 Upper DNN and Joint Training . . . . . . . . . . . . . . . . . 54
4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.4.1 Recognition System and Feature Extraction . . . . . . . . . . 56
4.4.2 Training and Structures of DNN-based Techniques . . . . . . 58
4.4.3 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 62
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
iv
5 Uncertainty-aware Training for DNN-HMM System using Varia-
tional Inference 67
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Uncertainty Decoding for Noise Robustness . . . . . . . . . . . . . . 72
5.3 Variational Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.4 VIF-based uncertainty-aware Training . . . . . . . . . . . . . . . . . 83
5.4.1 Clean Uncertainty Network . . . . . . . . . . . . . . . . . . . 91
5.4.2 Environment Uncertainty Network . . . . . . . . . . . . . . . 93
5.4.3 Prediction Network and Joint Training . . . . . . . . . . . . . 95
5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.5.1 Experimental Setup: Feature Extraction and ASR System . . 96
5.5.2 Network Structures . . . . . . . . . . . . . . . . . . . . . . . . 98
5.5.3 Eects of CUN on the Noise Robustness . . . . . . . . . . . . 104
5.5.4 Uncertainty Representation in Dierent SNR Condition . . . 105
5.5.5 Result of Speech Recognition . . . . . . . . . . . . . . . . . . 112
5.5.6 Result of Speech Recognition with LSTM-HMM . . . . . . . 114
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6 Conclusions 127
Bibliography 131
์์ฝ 145Docto
Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin
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