Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories

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 Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page