8,874 research outputs found

    Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays

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    In the analysis of acoustic scenes, often the occurring sounds have to be detected in time, recognized, and localized in space. Usually, each of these tasks is done separately. In this paper, a model-based approach to jointly carry them out for the case of multiple simultaneous sources is presented and tested. The recognized event classes and their respective room positions are obtained with a single system that maximizes the combination of a large set of scores, each one resulting from a different acoustic event model and a different beamformer output signal, which comes from one of several arbitrarily-located small microphone arrays. By using a two-step method, the experimental work for a specific scenario consisting of meeting-room acoustic events, either isolated or overlapped with speech, is reported. Tests carried out with two datasets show the advantage of the proposed approach with respect to some usual techniques, and that the inclusion of estimated priors brings a further performance improvement.Comment: Computational acoustic scene analysis, microphone array signal processing, acoustic event detectio

    Implementation and evaluation of a low complexity microphone array for speaker recognition

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    Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field

    ๊ฐ•์ธํ•œ ์Œ์„ฑ์ธ์‹์„ ์œ„ํ•œ DNN ๊ธฐ๋ฐ˜ ์Œํ–ฅ ๋ชจ๋ธ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๊น€๋‚จ์ˆ˜.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ•์ธํ•œ ์Œ์„ฑ์ธ์‹์„ ์œ„ํ•ด์„œ DNN์„ ํ™œ์šฉํ•œ ์Œํ–ฅ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํฌ๊ฒŒ ์„ธ ๊ฐ€์ง€์˜ DNN ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” DNN์ด ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์žก์Œ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ๊ฐ•์ธํ•จ์„ ๋ณด์กฐ ํŠน์ง• ๋ฒกํ„ฐ๋“ค์„ ํ†ตํ•˜์—ฌ ์ตœ๋Œ€๋กœ ํ™œ์šฉํ•˜๋Š” ์Œํ–ฅ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•์ด๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•์„ ํ†ตํ•˜์—ฌ DNN์€ ์™œ๊ณก๋œ ์Œ์„ฑ, ๊นจ๋—ํ•œ ์Œ์„ฑ, ์žก์Œ ์ถ”์ •์น˜, ๊ทธ๋ฆฌ๊ณ  ์Œ์†Œ ํƒ€๊ฒŸ๊ณผ์˜ ๋ณต์žกํ•œ ๊ด€๊ณ„๋ฅผ ๋ณด๋‹ค ์›ํ™œํ•˜๊ฒŒ ํ•™์Šตํ•˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ๊ธฐ๋ฒ•์€ Aurora-5 DB ์—์„œ ๊ธฐ์กด์˜ ๋ณด์กฐ ์žก์Œ ํŠน์ง• ๋ฒกํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๋ชจ๋ธ ์ ์‘ ๊ธฐ๋ฒ•์ธ ์žก์Œ ์ธ์ง€ ํ•™์Šต (noise-aware training, NAT) ๊ธฐ๋ฒ•์„ ํฌ๊ฒŒ ๋›ฐ์–ด๋„˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” DNN์„ ํ™œ์šฉํ•œ ๋‹ค ์ฑ„๋„ ํŠน์ง• ํ–ฅ์ƒ ๊ธฐ๋ฒ•์ด๋‹ค. ๊ธฐ์กด์˜ ๋‹ค ์ฑ„๋„ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” ์ „ํ†ต์ ์ธ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์ธ ๋น”ํฌ๋ฐ ๊ธฐ๋ฒ•์„ ํ†ตํ•˜์—ฌ ํ–ฅ์ƒ๋œ ๋‹จ์ผ ์†Œ์Šค ์Œ์„ฑ ์‹ ํ˜ธ๋ฅผ ์ถ”์ถœํ•˜๊ณ  ๊ทธ๋ฅผ ํ†ตํ•˜์—ฌ ์Œ์„ฑ์ธ์‹์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๊ธฐ์กด์˜ ๋น”ํฌ๋ฐ ์ค‘์—์„œ ๊ฐ€์žฅ ๊ธฐ๋ณธ์  ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ delay-and-sum (DS) ๋น”ํฌ๋ฐ ๊ธฐ๋ฒ•๊ณผ DNN์„ ๊ฒฐํ•ฉํ•œ ๋‹ค ์ฑ„๋„ ํŠน์ง• ํ–ฅ์ƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” DNN์€ ์ค‘๊ฐ„ ๋‹จ๊ณ„ ํŠน์ง• ๋ฒกํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๊ณต๋™ ํ•™์Šต ๊ธฐ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์™œ๊ณก๋œ ๋‹ค ์ฑ„๋„ ์ž…๋ ฅ ์Œ์„ฑ ์‹ ํ˜ธ๋“ค๊ณผ ๊นจ๋—ํ•œ ์Œ์„ฑ ์‹ ํ˜ธ์™€์˜ ๊ด€๊ณ„๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ multichannel wall street journal audio visual (MC-WSJAV) corpus์—์„œ์˜ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ, ๊ธฐ์กด์˜ ๋‹ค์ฑ„๋„ ํ–ฅ์ƒ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ถˆํ™•์ •์„ฑ ์ธ์ง€ ํ•™์Šต (Uncertainty-aware training, UAT) ๊ธฐ๋ฒ•์ด๋‹ค. ์œ„์—์„œ ์†Œ๊ฐœ๋œ ๊ธฐ๋ฒ•๋“ค์„ ํฌํ•จํ•˜์—ฌ ๊ฐ•์ธํ•œ ์Œ์„ฑ์ธ์‹์„ ์œ„ํ•œ ๊ธฐ์กด์˜ DNN ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•๋“ค์€ ๊ฐ๊ฐ์˜ ๋„คํŠธ์›Œํฌ์˜ ํƒ€๊ฒŸ์„ ์ถ”์ •ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ๊ฒฐ์ •๋ก ์ ์ธ ์ถ”์ • ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ด๋Š” ์ถ”์ •์น˜์˜ ๋ถˆํ™•์ •์„ฑ ๋ฌธ์ œ ํ˜น์€ ์‹ ๋ขฐ๋„ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ œ์•ˆํ•˜๋Š” UAT ๊ธฐ๋ฒ•์€ ํ™•๋ฅ ๋ก ์ ์ธ ๋ณ€ํ™” ์ถ”์ •์„ ํ•™์Šตํ•˜๊ณ  ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ์ธ ๋ณ€ํ™” ์˜คํ† ์ธ์ฝ”๋” (variational autoencoder, VAE) ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ๋‹ค. UAT๋Š” ์™œ๊ณก๋œ ์Œ์„ฑ ํŠน์ง• ๋ฒกํ„ฐ์™€ ์Œ์†Œ ํƒ€๊ฒŸ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๋งค๊ฐœํ•˜๋Š” ๊ฐ•์ธํ•œ ์€๋‹‰ ๋ณ€์ˆ˜๋ฅผ ๊นจ๋—ํ•œ ์Œ์„ฑ ํŠน์ง• ๋ฒกํ„ฐ ์ถ”์ •์น˜์˜ ๋ถ„ํฌ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ชจ๋ธ๋งํ•œ๋‹ค. UAT์˜ ์€๋‹‰ ๋ณ€์ˆ˜๋“ค์€ ๋”ฅ ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์Œํ–ฅ ๋ชจ๋ธ์— ์ตœ์ ํ™”๋œ uncertainty decoding (UD) ํ”„๋ ˆ์ž„์›Œํฌ๋กœ๋ถ€ํ„ฐ ์œ ๋„๋œ ์ตœ๋Œ€ ์šฐ๋„ ๊ธฐ์ค€์— ๋”ฐ๋ผ์„œ ํ•™์Šต๋œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ Aurora-4 DB์™€ CHiME-4 DB์—์„œ ๊ธฐ์กด์˜ DNN ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•๋“ค์„ ํฌ๊ฒŒ ๋›ฐ์–ด๋„˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค.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 ์š”์•ฝ 145Docto

    Efficacy in noise of the Starkey Surflink Mobile 2 technology in directional versus omnidirectional microphone mode with experienced adult hearing aid users

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    The Starkey SurfLink Mobile 2 is a remote microphone accessory. Starkey claims that by placing the SurfLinkโ€™s internal microphone in the directional microphone setting, the participant will hear better in noise over the omnidirectional setting. This study aims to test the thisthe claim about the devic
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