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19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018)

作者： Beck, Eugen Hannemann, Mirko Doetsch, Patrick Schlueter, Ralf Ney, Hermann Rhein Westfal TH Aachen Comp Sci Dept Human Language Technol & Pattern Recognit D-52062 Aachen Germany

ISBN: (纸本)9781510872219

It has been known for a long time that the classic Hidden-Markov-Model (HMM) derivation for speech recognition contains assumptions such as independence of observation vectors and weak duration modeling that are practical but unrealistic. When using the hybrid approach this is amplified by trying to fit a discriminative model into a generative one. Hidden Conditional Random Fields (CRFs) and segmental models (e.g. Semi-Markov CRFs / Segmental CRFs) have been proposed as an alternative, but for a long time have failed to get traction until recently. In this paper we explore different length modeling approaches for segmental models, their relation to attention-based systems. Furthermore we show experimental results on a handwriting recognition task and to the best of our knowledge the first reported results on the Switchboard 300h speech recognition corpus using this approach.

关键词： automatic speech recognition (ASR) (hidden) conditional random fields segmental models encoder-decoder attention mechanism

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encoder-decoder recurrent network model for interactive character animation generation

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VISUAL COMPUTER 2017年第6-8期33卷 971-980页

作者： Wang, Yumeng Che, Wujun Xu, Bo Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

In this paper, we propose a generative recurrent model for human-character interaction. Our model is an encoder-recurrent-decoder network. The recurrent network is composed by multiple layers of long short-term memory (LSTM) and is incorporated with an encoder network and a decoder network before and after the recurrent network. With the proposed model, the virtual character's animation is generated on the fly while it interacts with the human player. The coming animation of the character is automatically generated based on the history motion data of both itself and its opponent. We evaluated our model based on both public motion capture databases and our own recorded motion data. Experimental results demonstrate that the LSTM layers can help the character learn a long history of human dynamics to animate itself. In addition, the encoder-decoder networks can significantly improve the stability of the generated animation. This method can automatically animate a virtual character responding to a human player.

关键词： Human-character interaction Long short-term memory encoder-decoder Character animation Recurrent neural network Motion capture data

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Multi-step-ahead Host Load Prediction with GRU Based encoder-decoder in Cloud Computing 10

Multi-step-ahead Host Load Prediction with GRU Based Encoder...

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10th International Conference on Knowledge and Smart Technology (KST) - Cybernetics in the Next Decades

作者： Peng, Chenglei Li, Yang Yu, Yao Zhou, Yu Du, Sidan Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538640159

The details of the host workloads in cloud computing environment and the application demands of the real world computing system are becoming so complex that it throws a big challenge to the major cloud infrastructure vendors. To achieve service level agreements between users and cloud service vendors, it is essential to apply accurate prediction of future host load, which is also significant to improve the resource allocation and utilization in cloud computing. Although that there were several various methods and models developed, few of them can acquire the long-term temporal dependencies appropriately to make accurate predictions. In this paper, we apply a GRU based encoder-decoder network(GRUED) which contains two gated recurrent neural networks(GRUs) to address these issues. Thorough empirical studies based upon the Google resources usage traces and the traditional Unix system load traces demonstrate that our proposed method outperforms other state-of-the-art approaches for the prediction of multi-step-ahead host workload in cloud computing.

关键词： Cloud computing encoder-decoder Recurrent neural network Host load prediction GRU

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A COMPARISON OF TECHNIQUES FOR LANGUAGE MODEL INTEGRATION IN encoder-decoder SPEECH RECOGNITION

A COMPARISON OF TECHNIQUES FOR LANGUAGE MODEL INTEGRATION IN...

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IEEE Workshop on Spoken Language Technology (SLT)

作者： Toshniwal, Shubham Kannan, Anjuli Chiu, Chung-Cheng Wu, Yonghui Sainath, Tara N. Livescu, Karen Toyota Technol Inst Chicago IL 60637 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781538643341

Attention-based recurrent neural encoder-decoder models present an elegant solution to the automatic speech recognition problem. This approach folds the acoustic model, pronunciation model, and language model into a single network and requires only a parallel corpus of speech and text for training. However, unlike in conventional approaches that combine separate acoustic and language models, it is not clear how to use additional (unpaired) text. While there has been previous work on methods addressing this problem, a thorough comparison among methods is still lacking. In this paper, we compare a suite of past methods and some of our own proposed methods for using unpaired text data to improve encoder-decoder models. For evaluation, we use the medium-sized Switchboard data set and the large-scale Google voice search and dictation data sets. Our results confirm the benefits of using unpaired text across a range of methods and data sets. Surprisingly, for first-pass decoding, the rather simple approach of shallow fusion performs best across data sets. However, for Google data sets we find that cold fusion has a lower oracle error rate and outperforms other approaches after second-pass rescoring on the Google voice search data set.

关键词： speech recognition encoder-decoder language model shallow fusion cold fusion deep fusion

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T2S: An encoder-decoder Model for Topic-Based Natural Language Generation 23rd

T2S: An Encoder-Decoder Model for Topic-Based Natural Langua...

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23rd International Conference on Applications of Natural Language to Information Systems (NLDB)

作者： Ou, Wenjie Chen, Chaotao Ren, Jiangtao Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783319919478;9783319919461

Natural language generation (NLG) plays a critical role in various natural language processing (NLP) applications. And the topics provide a powerful tool to understand the natural language. We propose a novel topic-based NLG model which can generate topic coherent sentences given single topic or combination of topics. The model is an extension of the recurrent encoder-decoder framework by introducing a global topic embedding matrix. Experimental results show that our encoder can not only transform a source sentence to a representative topic distribution which can give a better interpretation of the source sentence, but also generate topic coherent and diversified sentences given different topic distribution without any text-level input.

关键词： Natural language generation Topic encoder-decoder

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Colorectal Segmentation using Multiple encoder-decoder Network in Colonoscopy Images 1

Colorectal Segmentation using Multiple Encoder-Decoder Netwo...

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1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Ngoc-Quang Nguyen Lee, Sang-Woong Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam South Korea

ISBN: (纸本)9781538695555

Colorectal cancer is the third most common cancer which causes of cancer-related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS-LariPolypDB [9] database shows that our proposed method outperforms state-of-the-art results.

关键词： colorectal segmentation CRC encoder-decoder multi-model

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DSNet:Multi-resolution Dense encoder and Stack decoder Network for Aerial Image Segmentation

DSNet:Multi-resolution Dense Encoder and Stack Decoder Netwo...

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Chinese Automation Congress (CAC)

作者： Chong, Yanwen Nie, Congchong Tao, Yulong Pan, Shaoming Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

ISBN: (纸本)9781728140940

Semantic segmentation in high resolution aerial image is faced with a challenge caused by ubiquitous fine-structure objects. Traditional encoder-decoder structure losses some detail information during the process of down-sampling, which is harmful to the location of fine-structure objects. In this work, we present a multi-resolution dense encoder and stack decoder network to deal with this problem. On the one hand, the dense encoder embeds shallow detailed feature into deep semantic feature through proposed information-reserved down-sampling method called CE-Pooling. On the other hand, the stack decoder gradually enhances the detailed feature through iterative attention fusion. Extensive experiments on several benchmark datasets have been conducted, which shows that our method is superior than the state-of-the-art approaches.

关键词： semantic segmentation encoder-decoder fine-structure ISPRS

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FEATURE FUSION encoder decoder NETWORK FOR AUTOMATIC LIVER LESION SEGMENTATION 16

FEATURE FUSION ENCODER DECODER NETWORK FOR AUTOMATIC LIVER L...

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16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Chen, Xueying Zhang, Rong Yang, Pingkun Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Anhui Peoples R China Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA

ISBN: (纸本)9781538636411

Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.51) and 3D, based on the dimensionality of the models. However, 2.51) and 31) methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a ***-decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion segmentation from CT images. Our feature fusion method is based on the attention mechanism, which fuses high-level features carrying semantic information with low-level features having image details. Additionally, to compensate for the information loss during the upsampling process, a dense upsampling convolution and a residual convolutional structure are proposed. We tested our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge and achieved competitive results compared with other state-of-the-art methods.

关键词： Liver lesion segmentation,deep learning encoder-decoder attention feature fusion

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ECTC-DOCD: An End-to-end Structure with CTC encoder and OCD decoder for Speech Recognition 20

ECTC-DOCD: An End-to-end Structure with CTC Encoder and OCD ...

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Interspeech Conference

作者： Yi, Cheng Wang, Feng Xu, Bo Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijinga Peoples R China

Real-time streaming speech recognition is required by most applications for a nice interactive experience. To naturally support online recognition, a common strategy used in recently proposed end-to-end models is to introduce a blank label to the label set and instead output alignments. However, generating the alignment means decoding much longer than the length of the linguistic sequence. Besides, there exist several blank labels between two output units in the alignment, which hinders models from learning the adjacent dependency of units in the target sequence. In this work, we propose an innovative encoder-decoder structure, called ECTC-DOCD, for online speech recognition which directly predicts the linguistic sequence without blank labels. Apart from the encoder and decoder structures, ECTC-DOCD contains an additional shrinking layer to drop the redundant acoustic information. This layer serves as a bridge connecting acoustic representation and linguistic modelling parts. Through experiments, we confirm that ECTC-DOCD can obtain better performance than a strong CTC model in online ASR tasks. We also show that ECTC-DOCD can achieve promising results on both Mandarin and English ASR datasets with first and second pass decoding.

关键词： end-to-end streaming ASR encoder-decoder OCD CTC

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encoder-decoder WITH FOCUS-MECHANISM FOR SEQUENCE LABELLING BASED SPOKEN LANGUAGE UNDERSTANDING

ENCODER-DECODER WITH FOCUS-MECHANISM FOR SEQUENCE LABELLING ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhu, Su Yu, Kai Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Key Lab Shanghai Educ Commiss Intelligent Interac SpeechLabDept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781509041176

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness

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