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CHARACTER-AWARE ATTENTION-BASED END-TO-END SPEECH RECOGNITION

CHARACTER-AWARE ATTENTION-BASED END-TO-END SPEECH RECOGNITIO...

引用

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

作者： Meng, Zhong Gaur, Yashesh Li, Jinyu Gong, Yifan Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781728103068

Predicting words and subword units (WSUs) as the output has shown to be effective for the attention-based encoder-decoder (AED) model in end-to-end speech recognition. However, as one input to the decoder recurrent neural network (RNN), each WSU embedding is learned independently through context and acoustic information in a purely data-driven fashion. Little effort has been made to explicitly model the morphological relationships among WSUs. In this work, we propose a novel character-aware (CA) AED model in which each WSU embedding is computed by summarizing the embeddings of its constituent characters using a CA-RNN. This WSU-independent CA-RNN is jointly trained with the encoder, the decoder and the attention network of a conventional AED to predict WSUs. With CA-AED, the embeddings of morphologically similar WSUs are naturally and directly correlated through the CA-RNN in addition to the semantic and acoustic relations modeled by a traditional AED. Moreover, CA-AED significantly reduces the model parameters in a traditional AED by replacing the large pool of WSU embeddings with a much smaller set of character embeddings. On a 3400 hours Microsoft Cortana dataset, CA-AED achieves up to 11.9% relative WER improvement over a strong AED baseline with 27.1% fewer model parameters.

关键词： character-aware end-to-end attention encoder-decoder speech recognition

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Multi-class Segmentation of Dark-Grown Arabidopsis Seedlings with U-Net 11

Multi-class Segmentation of Dark-Grown Arabidopsis Seedlings...

引用

11th International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Tong, Haonan Bullock, David Schmittling, Selene Haque, Samiul Tompkins, Vincent Chen, Hao Zhang, Chuang Stepanova, Anna N. Alonso, Jose M. Williams, Cranos M. Inspur Elect Informat Ind Co Ltd Beijing 100094 Peoples R China North Carolina State Univ Dept Plant & Microbial Biol Raleigh NC 27695 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9798350397871

Plant organ phenotyping represents a powerful tool to investigate the effects of biotic and abiotic factors on plant growth and development. Phenotyping typically involves the initial capture of high-resolution images of the plant of interest and the subsequent measurement of different morphometric parameters of specific plant organs. This second step is typically very time consuming and difficult to automate. To deal with this bottleneck, we developed a multi-class segmentation model based on U-shape network to identify hypocotyls and roots of young seedlings of the reference plant Arabidopsis thaliana. We applied a balanced cross entropy loss function to learn an alternative optimal network structure for this multi-class segmentation task. We evaluated our segmentation machine using 66 images of the wild-type Arabidopsis strain Col-0, as well as 34 images of the highly agravitropic and morphologically distinct mutant strain, aux1-7. Our model achieved a mean BFscore of 0.81 for Col-0 seedlings and 0.75 for aux1-7 mutant seedlings on the test dataset. Our model was also able to maintain accuracy in these two morphologically different genotypes suggesting that our segmentation procedure could be successfully applied to Arabidopsis seedlings showing broad morphological differences due to their genotype or treatment conditions. Appropriate segmentation is the first step in identifying phenotypic changes under hormone-mediated stress response. The identified growth parameters will be useful to identify the response associated with both abiotic and biotic stresses, which include but not limited to drought stress, heat stress, and the presence of pests or pathogens. The quantification of these parameters will aid assessment of genetic factors that contribute to the stress response.

关键词： Multi-class segmentation plant phenotype U-Net encoder-decoder deep learning

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MULTI-SCALE CROPPING MECHANISM FOR REMOTE SENSING IMAGE CAPTIONING 39

MULTI-SCALE CROPPING MECHANISM FOR REMOTE SENSING IMAGE CAPT...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhang, Xueting Wang, Qi Chen, Shangdong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781538691540

With the rapid development of artificial satellite, a large number of high resolution remote sensing images can be easily obtained now. Recently, remote sensing image captioning, which aims to generate accurate and concise descriptive sentences for remote sensing images, has been promoted by template-based model and encoder-decoder model with several related datasets released. Based on an encoder-decoder model, we propose a training mechanism of multi-scale cropping for remote sensing image captioning in this paper, which can extract more fine-grained information from remote sensing images and enhance the generalization performance of the base model. The experimental results on two datasets UCM-captions and Sydney-captions demonstrate that the proposed approach availably improves the performances in describing high resolution remote sensing images.

关键词： Remote sensing image image captioning encoder-decoder multi-scale cropping

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Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs) 34

Improving Quality of Observational Streaming Medical Data by...

引用

34th IEEE International Conference on Data Engineering Workshops (ICDEW)

作者： Bowie, Michael Begoli, Edmon Park, Byung H. Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA

ISBN: (纸本)9781538663066

We present an exploration of the encoder-decoder structured Long Short-Term Memory Network (LSTM) as a detector of the anomalous missing observations in streaming medical data by using the difference between the LSTM-reconstructed and observed values as the anomaly detector. We experiment with time-series data from bedside monitoring devices from the available Medical Information Mart for Intensive Care Database (MIMIC). Our results show that not only encoder-decoder LSTM approach works well for detecting the difference between anomalous and normal missing observations in streaming medical data, but also has an imputation potential for the missing data.

关键词： LSTM encoder-decoder anomaly detection missing data

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Network Traffic Prediction with Attention-based Spatial-Temporal Graph Network 24

Network Traffic Prediction with Attention-based Spatial-Temp...

引用

IEEE 24th International Conference on High Performance Switching and Routing, (HPSR)

作者： Peng, Yufei Guo, Yingya Hao, Run Lin, Junda Fuzhou Univ Dept Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou Peoples R China

ISBN: (纸本)9781665476409

Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention-based Spatial-Temporal Graph Network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.

关键词： Network Traffic Prediction Attention Mechanism Graph Neural Network Temporal and Spatial encoder-decoder

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Multi-Service Demand Forecasting using Graph Neural Networks 17

Multi-Service Demand Forecasting using Graph Neural Networks

引用

17th IEEE International Congress on Intelligent and Service-Oriented Systems Engineering (CISOSE)

作者： Theodoropoulos, Theodoros Makris, Antonios Kontopoulos, Ioannis Maroudis, Angelos-Christos Tserpes, Konstantinos Harokopio Univ Athens Dept Informat & Telemat Kallithea Greece

ISBN: (纸本)9798350322392

Accurate service demand forecasting is crucial towards achieving effective resource allocation and service orchestration. However, existing solutions require separate prediction models for each service, which consumes significant computation resources. In this paper, we propose a novel approach that introduces a global prediction model capable of generating accurate multi-step predictions involving multiple services and that can potentially leveraged for facilitating proactive strategies for orchestrating multiple services. The proposed model is based on an encoder-decoder architecture that utilizes Graph Neural Networks (GNNs) to capture interdependencies among input variables and their trends. By iteratively updating graph node representations, our model effectively incorporates historical trends and potential dependencies. Furthermore, the incorporation of GNNs into the encoder-decoder architecture enables the proposed approach to leverage the correlations among input variables, thus making it suitable for multivariate forecasting. Experimental results demonstrate the superiority of the proposed approach in terms of accurately conducting multi-step multiservice demand predictions, when compared against numerous contemporary deep learning models.

关键词： graph neural networks service demand forecasting edge deep learning encoder-decoder

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Sequence Prediction under Missing Data: An RNN Approach without Imputation 22

Sequence Prediction under Missing Data: An RNN Approach with...

引用

31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Pachal, Soumen Achar, Avinash IIT Madras Res Pk Consultancy Serv Res Chennai Tamil Nadu India

ISBN: (纸本)9781450392365

Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data directly without trying to impute data either before or during model building. Our encoding is lossless and achieves compression. It can be employed for both sequence classification and forecasting. We focus on forecasting here in a general context of multi-step prediction in presence of possible exogenous inputs. In particular, we propose novel variants of encoder-decoder (Seq2Seq) RNNs for this. The encoder here adopts the above mentioned pattern encoding, while at the decoder which has a different structure, multiple variants are feasible. We demonstrate the utility of our proposed architecture via multiple experiments on both single and multiple sequence (real) data-sets. We consider both scenarios where (i)data is naturally missing and (ii)data is synthetically masked.

关键词： Recurrent Neural Networks encoder-decoder Seq2Seq time-series Missing Data

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FANS: Fusing ASR and NLU for on-device SLU 22

FANS: Fusing ASR and NLU for on-device SLU

引用

Interspeech Conference

作者： Radfar, Martin Mouchtaris, Athanasios Kunzmann, Siegfried Rastrow, Ariya Amazon Alexa Machine Learning Seattle WA 98109 USA

ISBN: (纸本)9781713836902

Spoken language understanding (SLU) systems translate voice input commands to semantics which are encoded as an intent and pairs of slot tags and values. Most current SLU systems deploy a cascade of two neural models where the first one maps the input audio to a transcript (ASR) and the second predicts the intent and slots from the transcript (NLU). In this paper, we introduce FANS, a new end-to-end SLU model that fuses an ASR audio encoder to a multi-task NLU decoder to infer the intent, slot tags, and slot values directly from a given input audio, obviating the need for transcription. FANS consists of a shared audio encoder and three decoders, two of which are seq-to-seq decoders that predict non null slot tags and slot values in parallel and in an auto-regressive manner. FANS neural encoder and decoders architectures are flexible which allows us to leverage different combinations of LSTM, self-attention, and attenders. Our experiments show compared to the state-of-the-art end-to-end SLU models, FANS reduces ICER and IRER errors relatively by 30% and 7%, respectively, when tested on an in-house SLU dataset and by 0.86% and 2% absolute when tested on a public SLU dataset.

关键词： Spoken language understanding (SLU) Automatic speech recognition (ASR) Natural language understanding (NLU) sequence-to-sequence neural transformer encoder-decoder intent-slot

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SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE

SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE

引用

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

作者： Bonada, Jordi Blaauw, Merlijn Univ Pompeu Fabra Mus Technol Grp Barcelona Spain

ISBN: (纸本)9781728176055

We propose a semi-supervised singing synthesizer, which is able to learn new voices from audio data only, without any annotations such as phonetic segmentation. Our system is an encoder-decoder model with two encoders, linguistic and acoustic, and one (acoustic) decoder. In a first step, the system is trained in a supervised manner, using a labeled multi-singer dataset. Here, we ensure that the embeddings produced by both encoders are similar, so that we can later use the model with either acoustic or linguistic input features. To learn a new voice in an unsupervised manner, the pretrained acoustic encoder is used to train a decoder for the target singer. Finally, at inference, the pretrained linguistic encoder is used together with the decoder of the new voice, to produce acoustic features from linguistic input. We evaluate our system with a listening test and show that the results are comparable to those obtained with an equivalent supervised approach.

关键词： Singing synthesis semi-supervised encoder-decoder autoregressive convolutional

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Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network 22nd

Biomarker Localization by Combining CNN Classifier and Gener...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhang, Rong Tan, Shuhan Wang, Ruixuan Manivannan, Siyamalan Chen, Jingjing Lin, Haotian Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China MOE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China Univ Jaffna Dept Comp Sci Jaffna Sri Lanka Sun Yat Sen Univ Zhongshan Ophthalm Ctr Guangzhou Peoples R China

ISBN: (纸本)9783030322397;9783030322380

This paper proposes a novel deep neural network architecture to effectively localize potential biomarkers in medical images, when only the image-level labels are available during model training. The proposed architecture combines a CNN classifier and a generative adversarial network (GAN) in a novel way, such that the CNN classifier and the discriminator in the GAN can effectively help the encoder-decoder in the GAN to remove biomarkers. Biomarkers in abnormal images can then be easily localized and segmented by subtracting the output of the encoder-decoder from its original input. The proposed approach was evaluated on diabetic retinopathy images with real biomarkers and on skin images with simulated biomarkers, showing state-of-the-art performance in localizing biomarkers even if biomarkers are irregularly scattered and are of various sizes in images.

关键词： Biomarker localization encoder-decoder Generative adversarial networks

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