A Study of All-Convolutional Encoders for Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural networks (RNNs). We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as the "encoder" in CTC. CNNs lack an explicit representation of the entire sequence, but have the advantage that they are much faster to train. We present an exploration of CNNs as encoders for CTC models, in the context of character-based (lexicon-free) automatic speech recognition. In particular, we explore a range of one-dimensional convolutional layers, which are particularly efficient. We compare the performance of our CNN-based models against typical RNNbased models in terms of training time, decoding time, model size and word error rate (WER) on the Switchboard Eval2000 corpus. We find that our CNN-based models are close in performance to LSTMs, while not matching them, and are much faster to train and decode.Comment: Accepted to ICASSP-201

Making Good on LSTMs' Unfulfilled Promise

LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We examine an alternative called Continual Learning (CL), a memory-augmented approach, which can provide transparent explanations, i.e. which memory did what and when. This work has implications for many financial applications including credit, time-varying fairness in decision making and more. We make three important new observations. Firstly, as well as being more explainable, time-series CL approaches outperform LSTMs as well as a simple sliding window learner using feed-forward neural networks (FFNN). Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM). Thirdly, we examine how real-world, time-series noise impacts several similarity approaches used in CL memory addressing. We provide these insights using an approach called Continual Learning Augmentation (CLA) tested on a complex real-world problem, emerging market equities investment decision making. CLA provides a test-bed as it can be based on different types of time-series learners, allowing testing of LSTM and FFNN learners side by side. CLA is also used to test several distance approaches used in a memory recall-gate: Euclidean distance (ED), dynamic time warping (DTW), auto-encoders (AE) and a novel hybrid approach, warp-AE. We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

End-to-End Neural Network-based Speech Recognition for Mobile and Embedded Devices

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 성원용.Real-time automatic speech recognition (ASR) on mobile and embedded devices has been of great interest in recent years. Deep neural network-based automatic speech recognition demands a large number of computations, while the memory bandwidth and power storage of mobile devices are limited. The server-based implementation is often employed, but this increases latency or privacy concerns. Therefore, the need of the on-device ASR system is increasing. Recurrent neural networks (RNNs) are often used for the ASR model. The RNN implementation on embedded devices can suffer from excessive DRAM accesses, because the parameter size of a neural network usually exceeds that of the cache memory. Also, the parameters of RNN cannot be reused for multiple time-steps due to its feedback structure. To solve this problem, multi-time step parallelizable models are applied for speech recognition. The multi-time step parallelization approach computes multiple output samples at a time with the parameters fetched from the DRAM. Since the number of DRAM accesses can be reduced in proportion to the number of parallelization steps, a high processing speed can be achieved for the parallelizable model. In this thesis, a connectionist temporal classification (CTC) model is constructed by combining simple recurrent units (SRUs) and depth-wise 1-dimensional convolution layers for multi-time step parallelization. Both the character and word piece models are developed for the CTC model, and the corresponding RNN based language models are used for beam search decoding. A competitive WER for WSJ corpus is achieved using the entire model size of approximately 15MB. The system operates in real-time speed using only a single core ARM without GPU or special hardware. A low-latency on-device speech recognition system with a simple gated convolutional network (SGCN) is also proposed. The SGCN shows a competitive recognition accuracy even with 1M parameters. 8-bit quantization is applied to reduce the memory size and computation time. The proposed system features an online recognition with a 0.4s latency limit and operates in 0.2 RTF with only a single 900MHz CPU core. In addition, an attention-based model with the depthwise convolutional encoder is proposed. Convolutional encoders enable faster training and inference of attention models than recurrent neural network-based ones. However, convolutional models often require a very large receptive field to achieve high recognition accuracy, which not only increases the parameter size but also the computational cost and run-time memory footprint. A convolutional encoder with a short receptive field length often suffers from looping or skipping problems. We believe that this is due to the time-invariance of convolutions. We attempt to remedy this issue by adding positional information to the convolution-based encoder. It is shown that the word error rate (WER) of a convolutional encoder with a short receptive field size can be reduced significantly by augmenting it with positional information. Visualization results are presented to demonstrate the effectiveness of incorporating positional information. The streaming end-to-end ASR model is also developed by applying monotonic chunkwise attention.최근 모바일 및 임베디드 기기에서 실시간 동작하는 음성 인식 시스템을 개발하는 것이 큰 관심을 받고 있다. 깊은 인공 신경망 음성인식은 많은 양의 연산을 필요로 하는 반면, 모바일 기기의 메모리 대역폭이나 전력은 제한되어 있다. 이러한 한계 때문에 서버 기반 구현이 보통 사용되어지지만, 이는 지연 시간 및 사생활 침해 문제를 일으킨다. 따라서 모바일 기기 상 동작하는 음성 인식 시스템의 요구가 커지고 있다. 음성 인식 시스템에 주로 사용되는 모델은 재귀형 인공 신경망이다. 재귀형 인공 신경망의 모델 크기는 보통 캐시의 크기보다 크고 피드백 구조 때문에 재사용이 어렵기 때문에 많은 DRAM 접근을 필요로 한다. 이러한 문제를 해결하기 위해 다중 시간의 입력에대해 병렬화 가능한 모델을 이용한 음성 인식 시스템을 제안한다. 다중 시간 병렬화 기법은 한 번의 메모리 접근으로 여러 시간의 출력을 동시에 계산하는 방법이다. 병렬화 수에 따라 DRAM 접근 횟수를 줄일 수 있기 때문에, 병렬화 가능한 모델에 대하여 빠른 연산이 가능하다. 단순 재귀 유닛과 1차원 컨벌루션을 이용한 CTC 모델을 제시하였다. 문자와 단어 조각 수준의 모델이 개발되었다. 각 출력 단위에 해당하는 재귀형 신경망 기반 언어 모델을 이용하여 디코딩에 사용되었다. 전체 15MB의 메모리 크기로 WSJ 에서 높은 수준의 인식 성능을 얻었으며 GPU나 기타 하드웨어 없이 1개의 ARM CPU 코어로 실시간 처리를 달성하였다. 또한 단순 컨벌루션 인공 신경망 (SGCN)을 이용한 낮은 지연시간을 가지는 음성인식 시스템을 개발하였다. SGCN은 1M의 매우 낮은 변수 갯수로도 경쟁력 있는 인식 정확도를 보여준다. 추가적으로 8-bit 양자화를 적용하여 메모리 크기와 연산 시간을 감소 시켰다. 해당 시스템은 0.4초의 이론적 지연시간을 가지며 900MHz의 CPU 상에서 0.2의 RTF로 동작하였다. 추가적으로, 깊이별 컨벌루션 인코더를 이용한 어텐션 기반 모델이 개발되었다. 컨벌루션 기반의 인코더는 재귀형 인공 신경망 기반 모델보다 빠른 처리 속도를 가진다. 하지만 컨벌루션 모델은 높은 성능을 위해서 큰 입력 범위를 필요로 한다. 이는 모델 크기 및 연산량, 그리고 동작 시 메모리 소모를 증가 시킨다. 작은 크기의 입력 범위를 가지는 컨벌루션 인코더는 출력의 반복이나 생략으로 인하여 높은 오차율을 가진다. 이것은 컨벌루션의 시간 불변성 때문으로 여겨지며, 이 문제를 위치 인코딩 벡터를 이용하여 해결하였다. 위치 정보를 이용하여 작은 크기의 필터를 가지는 컨벌루션 모델의 성능을 높일 수 있음을 보였다. 또한 위치 정보가 가지는 영향을 시각화 하였다. 해당 방법을 단조 어텐션을 이용한 모델에 활용하여 컨벌루션 기반의 스트리밍 가능한 음성 인식 시스템을 개발하였다.1 Introduction 1 1.1 End-to-End Automatic Speech Recognition with Neural Networks . . 1 1.2 Challenges on On-device Implementation of Neural Network-based ASR 2 1.3 Parallelizable Neural Network Architecture 3 1.4 Scope of Dissertation 3 2 Simple Recurrent Units for CTC-based End-to-End Speech Recognition 6 2.1 Introduction 6 2.2 Related Works 8 2.3 Speech Recognition Algorithm 9 2.3.1 Acoustic modeling 10 2.3.2 Character-based model 12 2.3.3 Word piece-based model 14 2.3.4 Decoding 14 2.4 Experimental Results 15 2.4.1 Acoustic models 15 2.4.2 Word piece based speech recognition 22 2.4.3 Execution time analysis 25 2.5 Concluding Remarks 27 3 Low-Latency Lightweight Streaming Speech Recognition with 8-bit Quantized Depthwise Gated Convolutional Neural Networks 28 3.1 Introduction 28 3.2 Simple Gated Convolutional Networks 30 3.2.1 Model structure 30 3.2.2 Multi-time-step parallelization 31 3.3 Training CTC AM with SGCN 34 3.3.1 Regularization with symmetrical weight noise injection 34 3.3.2 8-bit quantization 34 3.4 Experimental Results 36 3.4.1 Experimental setting 36 3.4.2 Results on WSJ eval92 38 3.4.3 Implementation on the embedded system 38 3.5 Concluding Remarks 39 4 Effect of Adding Positional Information on Convolutional Neural Networks for End-to-End Speech Recognition 41 4.1 Introduction 41 4.2 Related Works 43 4.3 Model Description 45 4.4 Experimental Results 46 4.4.1 Effect of receptive field size 46 4.4.2 Visualization 49 4.4.3 Comparison with other models 53 4.5 Concluding Remarks 53 5 Convolution-based Attention Model with Positional Encoding for Streaming Speech Recognition 55 5.1 Introduction 55 5.2 Related Works 58 5.3 End-to-End Model for Speech Recognition 61 5.3.1 Model description 61 5.3.2 Monotonic chunkwise attention 62 5.3.3 Positional encoding 63 5.4 Experimental Results 64 5.4.1 Effect of positional encoding 66 5.4.2 Comparison with other models 68 5.4.3 Execution time analysis 70 5.5 Concluding Remarks 71 6 Conclusion 72 Abstract (In Korean) 86Docto

Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition

We propose a novel approach to semi-supervised automatic speech recognition (ASR). We first exploit a large amount of unlabeled audio data via representation learning, where we reconstruct a temporal slice of filterbank features from past and future context frames. The resulting deep contextualized acoustic representations (DeCoAR) are then used to train a CTC-based end-to-end ASR system using a smaller amount of labeled audio data. In our experiments, we show that systems trained on DeCoAR consistently outperform ones trained on conventional filterbank features, giving 42% and 19% relative improvement over the baseline on WSJ eval92 and LibriSpeech test-clean, respectively. Our approach can drastically reduce the amount of labeled data required; unsupervised training on LibriSpeech then supervision with 100 hours of labeled data achieves performance on par with training on all 960 hours directly. Pre-trained models and code will be released online.Comment: Accepted to ICASSP 2020 (oral

Recurrent Attention Models for Depth-Based Person Identification

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201