226 research outputs found
An extended experimental investigation of DNN uncertainty propagation for noise robust ASR
International audienceAutomatic speech recognition (ASR) in noisy environments remains a challenging goal. Recently, the idea of estimating the uncertainty about the features obtained after speech enhancement and propagating it to dynamically adapt deep neural network (DNN) based acoustic models has raised some interest. However, the results in the literature were reported on simulated noisy datasets for a limited variety of uncertainty estimators. We found that they vary significantly in different conditions. Hence, the main contribution of this work is to assess DNN uncertainty decoding performance for different data conditions and different uncertainty estimation/propagation techniques. In addition, we propose a neural network based uncertainty estima-tor and compare it with other uncertainty estimators. We report detailed ASR results on the CHiME-2 and CHiME-3 datasets. We find that, on average, uncertainty propagation provides similar relative improvement on real and simulated data and that the proposed uncertainty estimator performs significantly better than the one in [1]. We also find that the improvement is consistent, but it depends on the signal-to-noise ratio (SNR) and the noise environment
๊ฐ์ธํ ์์ฑ์ธ์์ ์ํ DNN ๊ธฐ๋ฐ ์ํฅ ๋ชจ๋ธ๋ง
ํ์๋
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
Deep Learning for Distant Speech Recognition
Deep learning is an emerging technology that is considered one of the most
promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech
represents a crucial leap towards intelligent machines. Despite the great
efforts of the past decades, however, a natural and robust human-machine speech
interaction still appears to be out of reach, especially when users interact
with a distant microphone in noisy and reverberant environments. The latter
disturbances severely hamper the intelligibility of a speech signal, making
Distant Speech Recognition (DSR) one of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques,
architectures, and algorithms to improve the robustness of distant-talking
acoustic models. We first elaborate on methodologies for realistic data
contamination, with a particular emphasis on DNN training with simulated data.
We then investigate on approaches for better exploiting speech contexts,
proposing some original methodologies for both feed-forward and recurrent
neural networks. Lastly, inspired by the idea that cooperation across different
DNNs could be the key for counteracting the harmful effects of noise and
reverberation, we propose a novel deep learning paradigm called network of deep
neural networks. The analysis of the original concepts were based on extensive
experimental validations conducted on both real and simulated data, considering
different corpora, microphone configurations, environments, noisy conditions,
and ASR tasks.Comment: PhD Thesis Unitn, 201
Deep Speaker Feature Learning for Text-independent Speaker Verification
Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw features. This paper presents a convolutional time-delay deep
neural network structure (CT-DNN) for speaker feature learning. Our
experimental results on the Fisher database demonstrated that this CT-DNN can
produce high-quality speaker features: even with a single feature (0.3 seconds
including the context), the EER can be as low as 7.68%. This effectively
confirmed that the speaker trait is largely a deterministic short-time property
rather than a long-time distributional pattern, and therefore can be extracted
from just dozens of frames.Comment: deep neural networks, speaker verification, speaker featur
Data-driven Speech Intelligibility Enhancement and Prediction for Hearing Aids
Hearing impairment is a widespread problem around the world. It is estimated that one in six people are living with some degree of hearing loss. Moderate and severe hearing impairment has been recognised as one of the major causes of disability, which is associated with declines in the quality of life, mental illness and dementia. However, investigation shows that only 10-20\% of older people with significant hearing impairment wear hearing aids. One of the main factors causing the low uptake is that current devices struggle to help hearing aid users understand speech in noisy environments. For the purpose of compensating for the elevated hearing thresholds and dysfunction of source separation processing caused by the impaired auditory system, amplification and denoising have been the major focuses of current hearing aid studies to improve the intelligibility of speech in noise. Also, it is important to derive a metric that can fairly predict speech intelligibility for the better development of hearing aid techniques.
This thesis aims to enhance the speech intelligibility of hearing impaired listeners. Motivated by the success of data-driven approaches in many speech processing applications, this work proposes the differentiable hearing aid speech processing (DHASP) framework to optimise both the amplification and denoising modules within a hearing aid processor. This is accomplished by setting an intelligibility-based optimisation objective and taking advantage of large-scale speech databases to train the hearing aid processor to maximise the intelligibility for the listeners. The first set of experiments is conducted on both clean and noisy speech databases, and the results from objective evaluation suggest that the amplification fittings optimised within the DHASP framework can outperform a widely used and well-recognised fitting. The second set of experiments is conducted on a large-scale database with simulated domestic noisy scenes. The results from both objective and subjective evaluations show that the DHASP-optimised hearing aid processor incorporating a deep neural network-based denoising module can achieve competitive performance in terms of intelligibility enhancement.
A precise intelligibility predictor can provide reliable evaluation results to save the cost of expensive and time-consuming subjective evaluation. Inspired by the findings that automatic speech recognition (ASR) models show similar recognition results as humans in some experiments, this work exploits ASR models for intelligibility prediction. An intrusive approach using ASR hidden representations and a non-intrusive approach using ASR uncertainty are proposed and explained in the third and fourth experimental chapters. Experiments are conducted on two databases, one with monaural speech in speech-spectrum-shaped noise with normal hearing listeners, and the other one with processed binaural speech in domestic noise with hearing impaired listeners. Results suggest that both the intrusive and non-intrusive approaches can achieve top performances and outperform a number of widely used intelligibility prediction approaches.
In conclusion, this thesis covers both the enhancement and prediction of speech intelligibility for hearing aids. The proposed hearing aid processor optimised within the proposed DHASP framework can significantly improve the intelligibility of speech in noise for hearing impaired listeners. Also, it is shown that the proposed ASR-based intelligibility prediction approaches can achieve state-of-the-art performances against a number of widely used intelligibility predictors
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Optimisation Methods For Training Deep Neural Networks in Speech Recognition
Automatic Speech Recognition (ASR) is an example of a sequence to sequence level classification task where, given an acoustic waveform, the goal is to produce the correct word level hypotheses. In machine learning, a classification problem such as ASR is solved in two stages: an inference stage that models the uncertainty associated with the choice of hypothesis given the acoustic waveform using a mathematical model, and a decision stage which employs the inference model in conjunction with decision theory to make optimal class assignments. With the advent of careful network initialisation and GPU computing, hybrid Hidden Markov Models (HMMs) augmented with Deep Neural Networks (DNNs) have shown to outperform traditional HMMs using Gaussian Mixture Models (GMMs) in solving the inference problem for ASR. In comparison to GMMs, DNNs possess a better capability to model the underlying non-linear data manifold due to their deep and complex structure. While the structure of such models gives rich modelling capability, it also creates complex dependencies between the parameters which can make learning difficult via first order stochastic gradient descent (SGD). The task of finding the best procedure to train DNNs continues to be an active area of research and has been made even more challenging by the availability of ever more training data. This thesis focuses on designing better optimisation approaches to train hybrid HMM-DNN models using sequence level discriminative criterion which is a natural loss function that preserves the sequential ordering of frames within a spoken utterance. The thesis presents an implementation of the second order Hessian Free (HF) optimisation method, and shows how the method can made efficient through appropriate modifications to the Conjugate Gradient algorithm. To achieve better convergence than SGD, this work explores the Natural Gradient method to train DNNs with discriminative sequence training. In the DNN literature, the method has been applied to train models for the Maximum Likelihood objective criterion. A novel contribution of this thesis is to extend this approach to the domain of Minimum Bayes Risk objective functions for discriminative sequence training. With sigmoid models trained on a 50hr and 200hr training set from the Multi-Genre Broadcast 1 (MGB1) transcription task, the NG method applied in a HF styled optimisation framework is shown to achieve better Word Error Rate (WER) reductions on the MGB1 development set than SGD from sequence training.
This thesis also addresses the particular issue of overfitting between the training criterion and WER, that primarily arises during sequence training of DNN models that use Rectified Linear Units (ReLUs) as activation functions. It is shown how by scaling with the Gauss Newton matrix, the HF method unlike other approaches can overcome this issue. Seeing that different optimisers work best with different models, it is attractive to have a consistent optimisation framework that is agnostic to the choice of activation function. To address the issue, this thesis develops the geometry of the underlying function space captured by different realisations of DNN model parameters, and presents the design considerations for an optimisation algorithm to be well defined on this space. Building on this analysis, a novel optimisation technique called NGHF is presented that uses both the direction of steepest descent on a probabilistic manifold and local curvature information to effectively probe the error surface. The basis of the method relies on an alternative derivation of Taylorโs theorem using the concepts of manifolds, tangent vectors and directional derivatives from the perspective of Information Geometry. Apart from being well defined on the function space, when framed within a HF style optimisation framework, the method of NGHF is shown to achieve the greatest WER reductions from sequence training on the MGB1 development set with both sigmoid and ReLU based models trained on the 200hr MGB1 training set. The evaluation of the above optimisation methods in training different DNN model architectures is also presented.IDB Cambridge International Scholarshi
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