Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Very Deep Convolutional Neural Networks for Robust Speech Recognition

This paper describes the extension and optimization of our previous work on very deep convolutional neural networks (CNNs) for effective recognition of noisy speech in the Aurora 4 task. The appropriate number of convolutional layers, the sizes of the filters, pooling operations and input feature maps are all modified: the filter and pooling sizes are reduced and dimensions of input feature maps are extended to allow adding more convolutional layers. Furthermore appropriate input padding and input feature map selection strategies are developed. In addition, an adaptation framework using joint training of very deep CNN with auxiliary features i-vector and fMLLR features is developed. These modifications give substantial word error rate reductions over the standard CNN used as baseline. Finally the very deep CNN is combined with an LSTM-RNN acoustic model and it is shown that state-level weighted log likelihood score combination in a joint acoustic model decoding scheme is very effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%, further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN joint decoding.Comment: accepted by SLT 201

강인한 음성인식을 위한 DNN 기반 음향 모델링

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 김남수.본 논문에서는 강인한 음성인식을 위해서 DNN을 활용한 음향 모델링 기법들을 제안한다. 본 논문에서는 크게 세 가지의 DNN 기반 기법을 제안한다. 첫 번째는 DNN이 가지고 있는 잡음 환경에 대한 강인함을 보조 특징 벡터들을 통하여 최대로 활용하는 음향 모델링 기법이다. 이러한 기법을 통하여 DNN은 왜곡된 음성, 깨끗한 음성, 잡음 추정치, 그리고 음소 타겟과의 복잡한 관계를 보다 원활하게 학습하게 된다. 본 기법은 Aurora-5 DB 에서 기존의 보조 잡음 특징 벡터를 활용한 모델 적응 기법인 잡음 인지 학습 (noise-aware training, NAT) 기법을 크게 뛰어넘는 성능을 보였다. 두 번째는 DNN을 활용한 다 채널 특징 향상 기법이다. 기존의 다 채널 시나리오에서는 전통적인 신호 처리 기법인 빔포밍 기법을 통하여 향상된 단일 소스 음성 신호를 추출하고 그를 통하여 음성인식을 수행한다. 우리는 기존의 빔포밍 중에서 가장 기본적 기법 중 하나인 delay-and-sum (DS) 빔포밍 기법과 DNN을 결합한 다 채널 특징 향상 기법을 제안한다. 제안하는 DNN은 중간 단계 특징 벡터를 활용한 공동 학습 기법을 통하여 왜곡된 다 채널 입력 음성 신호들과 깨끗한 음성 신호와의 관계를 효과적으로 표현한다. 제안된 기법은 multichannel wall street journal audio visual (MC-WSJAV) corpus에서의 실험을 통하여, 기존의 다채널 향상 기법들보다 뛰어난 성능을 보임을 확인하였다. 마지막으로, 불확정성 인지 학습 (Uncertainty-aware training, UAT) 기법이다. 위에서 소개된 기법들을 포함하여 강인한 음성인식을 위한 기존의 DNN 기반 기법들은 각각의 네트워크의 타겟을 추정하는데 있어서 결정론적인 추정 방식을 사용한다. 이는 추정치의 불확정성 문제 혹은 신뢰도 문제를 야기한다. 이러한 문제점을 극복하기 위하여 제안하는 UAT 기법은 확률론적인 변화 추정을 학습하고 수행할 수 있는 뉴럴 네트워크 모델인 변화 오토인코더 (variational autoencoder, VAE) 모델을 사용한다. UAT는 왜곡된 음성 특징 벡터와 음소 타겟과의 관계를 매개하는 강인한 은닉 변수를 깨끗한 음성 특징 벡터 추정치의 분포 정보를 이용하여 모델링한다. UAT의 은닉 변수들은 딥 러닝 기반 음향 모델에 최적화된 uncertainty decoding (UD) 프레임워크로부터 유도된 최대 우도 기준에 따라서 학습된다. 제안된 기법은 Aurora-4 DB와 CHiME-4 DB에서 기존의 DNN 기반 기법들을 크게 뛰어넘는 성능을 보였다.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 요약 145Docto

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.Comment: Submitted to IEEE Open Journal of Signal Processing. 30 pages, 27 figure

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks