Query: Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models

Relevant passages:
On speaker adaptation of long short-term memory recurrent neural networks <=SEP=> Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture specializing in modeling long-range temporal dynamics. On acoustic modeling tasks, LSTM-RNNs have shown better performance than DNNs and conventional RNNs. In this paper, we conduct an extensive study on speaker adaptation of LSTM-RNNs. Speaker adaptation helps to reduce the mismatch between acoustic models and testing speakers. We have two main goals for this study. First, on a benchmark dataset, the existing DNN adaptation techniques are evaluated on the adaptation of LSTM-RNNs. We observe that LSTMRNNs can be effectively adapted by using speaker-adaptive (SA) front-end, or by inserting speaker-dependent (SD) layers. Second, we propose two adaptation approaches that implement the SD-layer-insertion idea specifically for LSTM-RNNs. Using these approaches, speaker adaptation improves word error rates by 3-4% relative over a strong LSTM-RNN baseline. This improvement is enlarged to 6-7% if we exploit SA features for further adaptation.

KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition <=SEP=> We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The technique developed in this paper adapts the model conservatively by forcing the senone distribution estimated from the adapted model to be close to that from the unadapted model. This constraint is realized by adding Kullback-Leibler divergence (KLD) regularization to the adaptation criterion. We show that applying this regularization is equivalent to changing the target distribution in the conventional backpropagation algorithm. Experiments on Xbox voice search, short message dictation, and Switchboard and lecture speech transcription tasks demonstrate that the proposed adaptation technique can provide 2%-30% relative error reduction against the already very strong speaker independent CD-DNN-HMM systems using different adaptation sets under both supervised and unsupervised adaptation setups.

Chainer : a Next-Generation Open Source Framework for Deep Learning <=SEP=> Software frameworks for neural networks play key roles in the development and application of deep learning methods. However, as new types of deep learning models are developed, existing frameworks designed for convolutional neural networks are becoming less useful. In this paper, we introduce Chainer, a Pythonbased, standalone open source framework for deep learning models. Chainer provides a flexible, intuitive, and high performance means of implementing a full range of deep learning models, including state-of-the-art models such as recurrent neural networks and variational autoencoders.

Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription <=SEP=> We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third—from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.

DeepPose: Human Pose Estimation via Deep Neural Networks <=SEP=> We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.

Multi-Graph Learning with Positive and Unlabeled Bags <=SEP=> In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance.

Superimposed Sparse Parameter Classifiers for Face Recognition <=SEP=> In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is proposed for face recognition. SSP is motivated by two phase test sample sparse representation (TPTSSR) and linear regression classification (LRC), which can be treated as the extended of sparse representation classification (SRC). SRC uses all the train samples to produce the sparse representation vector for classification. The LRC, which can be interpreted as L2-norm sparse representation, uses the distances between the test sample and the class subspaces for classification. TPTSSR is also L2-norm sparse representation and uses two phase to compute the distance for classification. Instead of the distances, the SSP classifier employs the SSPs, which can be expressed as the sum of the linear regression parameters of each class in iterations, is used for face classification. Further, the fast SSP (FSSP) classifier is also suggested to reduce the computation cost. A mass of experiments on Georgia Tech face database, ORL face database, CVL face database, AR face database, and CASIA face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than the LRC, SRC, collaborative representation-based classification, regularized robust coding, relaxed collaborative representation, support vector machine, and TPTSSR for face recognition under various conditions.

Face Recognition Machine Vision System Using Eigenfaces <=SEP=> Face Recognition is a common problem in Machine Learning. This technology has already been widely used in our lives. For example, Facebook can automatically tag people’s faces in images, and also some mobile devices use face recognition to protect private security. Face images comes with different background, variant illumination, different facial expression and occlusion. There are a large number of approaches for the face recognition. Different approaches for face recognition have been experimented with specific databases which consist of single type, format and composition of image. Doing so, these approaches don’t suit with different face databases. One of the basic face recognition techniques is eigenface which is quite simple, efficient, and yields generally good results in controlled circumstances. So, this paper presents an experimental performance comparison of face recognition using Principal Component Analysis (PCA) and Normalized Principal Component Analysis (NPCA). The experiments are carried out on the ORL (ATT) and Indian face database (IFD) which contain variability in expression, pose, and facial details. The results obtained for the two methods have been compared by varying the number of training images. MATLAB is used for implementing algorithms also.

Analog Imagery in Mental Model Reasoning: Depictive Models <=SEP=> We investigated whether people can use analog imagery to model the behavior of a simple mechanical interaction. Subjects saw a static computer display of two touching gears that had different diameters. Their task was to determine whether marks on each gear would meet if the gears rotated inward. This task added a problem of coordination to the typical analog rotation task in that the gears had a physical interdependency; the angular velocity of one gear depended on the angular velocity of the other gear. In the first experiment, we found the linear relationship between response time and angular disparity that indicates analog imagery. In the second experiment, we found that people can also solve the problem through a non-analog, visual comparison. We also found that people of varying spatial ability could switch between analog and non-analog solutions if instructed to do so. In the third experiment, we examined whether the elicitation of physical knowledge would influence solution strategies. To do so, we manipulated the visual realism of the gear display. Subjects who saw the most realistic gears coordinated their transformations by using the surfaces of the gears, as though they were relying on the friction connecting the surfaces. Subjects who saw more schematic displays relied on analytic strategies, such as comparing the ratios made by the angles and/or diameters of the two gears. To explain the relationship between spatial and physical knowledge found in the experiments, we constructed a computer simulation of what we call depictive modeling. In a depictive model, general spatial knowledge and context-sensitive physical knowledge have the same ontology. This is different from prior simulations in which a non-analog representation would be needed to coordinate the analog behaviors of physical objects. In our simulation, the inference that coordinates the gear motions emerges from the analog rotations themselves. We suggest that mental depictions create a bridge between imagery and mental model research by positing the referent as the primary conceptual entity.

Mining Public Transport User Behaviour from Smart Card Data <=SEP=> In urban public transport, smart card data is made of millions of observations of users boarding vehicles over the network across several days. The issue addresses whether data mining techniques can be used to study user behaviour from these observations. This must be done with the help of transportation planning knowledge. Hence, this paper presents a common “transportation planning/data mining” methodology for user behaviour analysis. Experiments were conducted on data from a Canadian transit authority. This experience demonstrates that a combination of planning knowledge and data mining tool allows producing travel behaviours indicators, mainly regarding regularity and daily patterns, from data issued from operational and management system. Results show that the public transport users of this study can rapidly be divided in four major behavioural groups, whatever type of ticket they use. Copyright © 2006 IFAC.

stagNet: An Attentive Semantic RNN for Group Activity Recognition <=SEP=> Group activity recognition plays a fundamental role in a variety of applications, e.g. sports video analysis and intelligent surveillance. How to model the spatio-temporal contextual information in a scene still remains a crucial yet challenging issue. We propose a novel attentive semantic recurrent neural network (RNN), dubbed as stagNet, for understanding group activities in videos, based on the spatio-temporal attention and semantic graph. A semantic graph is explicitly modeled to describe the spatial context of the whole scene, which is further integrated with the temporal factor via structural-RNN. Benefiting from the ‘factor sharing’ and ‘message passing’ mechanisms, our model is capable of extracting discriminative spatio-temporal features and capturing inter-group relationships. Moreover, we adopt a spatio-temporal attention model to attend to key persons/frames for improved performance. Two widely-used datasets are employed for performance evaluation, and the extensive results demonstrate the superiority of our method.

Intelligent stereo camera mobile platform for indoor service robot research <=SEP=> Stereo vision is an active research topic in computer vision. Point Grey® Bumblebee® and digital single-lens reflex camera (DSLR) are normally found in the stereo vision research, they are robust but expensive. Open source electronic prototyping platforms such as Arduino and Raspberry Pi are interesting products, which allows students or researchers to custom made inexpensive experimental equipment for their research projects. This paper describes the intelligent stereo camera mobile platform developed in our research using Pi and camera modules and presents the concept of using inexpensive open source parts for robotic stereo vision research work in details.

Merging and Splitting Eigenspace Models <=SEP=> We present new deterministic methods that given two eigenspace models, each representing a set of n-dimensional observations will: (1) merge the models to yield a representation of the union of the sets; (2) split one model from another to represent the difference between the sets; as this is done, we accurately keep track of the mean. These methods are more efficient than computing new eigenspace models directly from the observations when the eigenmodels are dimensionally small compared to the total number of observations. Such methods are important because they provide a basis for novel techniques in machine learning, using a dynamic split-andmerge paradigm to optimally cluster observations. Here we present a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the on-line construction of Gaussian mixture models.

An Embedded Declarative Language for Hierarchical Object Structure Traversal <=SEP=> A common challenge in processing large domain-specific models and in-memory object structures (e.g., complex XML documents) is writing traversals and queries on them. Object-oriented (OO) designs, particularly those based on the Visitor pattern, are commonly used for developing traversals. However, such OO designs limit the reusability and independent evolution of visitation actions (i.e., the actions to be performed at each traversed node) due to tight coupling between the traversal logic and visitation actions, particularly when a variety of different traversals are needed. Code generators developed for traversal specification languages alleviate some of these problems but their high cost of development is often prohibitive. This paper presents Language for Embedded quEry and traverSAl (LEESA), which provides a generative programming approach for embedding object structure queries and traversal specifications within a host language, C++. By virtue of being declarative, LEESA significantly reduces the development cost of programs operating on complex object structures compared to the traditional techniques.

Design of Broadband Linear and Efficient Power Amplifier for Long-Term Evolution Applications <=SEP=> In this letter, a broadband linear and efficient power amplifier for base-stations in long-term evolution (LTE) applications is designed, fabricated, and experimentally verified, employing Cree's CGH40010F GaN HEMT. A novel method of baseband, fundamental and harmonic impedances control is used to provide high linearity and efficiency across a broad bandwidth. When producing a two-tone carrier-to-intermodulation ratio (C/I) of 30 dBc, the circuit has demonstrated a two-tone power added efficiency (PAE) between 45%-60% across the frequency range from 1.6 to 2.6 GHz while delivering 36.0-38.5 dBm average output power. For a single-carrier 20 MHz LTE signal with a peak-to-average ratio (PAR) of 6.5 dB, a measured high PAE of 40%-55% can be achieved at an average output power of 35.3-37.5 dBm with an adjacent channel leakage ratio (ACLR) of about -30 dBc from 1.6 to 2.6 GHz.

Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging <=SEP=> Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.

Wireless health monitoring system for patients <=SEP=> eHealth is the provision of healthcare with the inclusion of telecommunication techniques. This project looks at the construction of a simple device that will be capable of transferring the data of a patient's vital signs to a remote device wirelessly. The necessity of this project is to alleviate the difficulty that is encountered by medical experts in monitoring multiple patients simultaneously. This project will enable them to observe patients without having to be physically present at their bedside, be it in the hospital or in their home. A patient's body temperature, heart rate and electrocardiography (ECG) are transferred wirelessly through an agent such as Bluetooth technology.

Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling <=SEP=> Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs, using back-propagation through time, often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.

SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions <=SEP=> In order to leverage the full potential of the Semantic Web it is necessary to transparently query distributed RDF data sources in the same way as it has been possible with federated databases for ages. However, there are significant differences between the Web of (linked) Data and the traditional database approaches. Hence, it is not straightforward to adapt successful database techniques for RDF federation. Reasons are the missing cooperation between SPARQL endpoints and the need for detailed data statistics for estimating the costs of query execution plans. We have implemented SPLENDID, a query optimization strategy for federating SPARQL endpoints based on statistical data obtained from voiD descriptions [1].

tress and anxiety detection using facial cues from videos <=SEP=> This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camerabased photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states nxiety tress link rate ead motion with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.

Industrial IoT lifecycle via digital twins <=SEP=> Currently, the IoT discussion is focused primarily on the operational phase. This includes how a IoT device behaves, operates, communicates, and interacts with other IoT devices during operation. However, IoT devices and systems have other lifecycle phases before and after operation. This extended abstract provides an overview of how other IoT lifecycle phases (e.g., design and service) can be improved with information feedback and feedforward flows between them. Digital Twins are a new mechanism to manage IoT devices and IoT systems-of-systems throughout their lifecycle. We present our vision on the industrial IoT lifecycle managed and optimized at scale via Digital Twins.

A Feature Learning Based Approach for Automated Fruit Yield Estimation <=SEP=> This paper demonstrates a generalised multi-scale feature learning approach to multi-class segmentation, applied to the estimation of fruit yield on treecrops. The learning approach makes the algorithm flexible and adaptable to different classification problems, and hence applicable to a wide variety of tree-crop applications. Extensive experiments were performed on a dataset consisting of 8000 colour images collected in an apple orchard. This paper shows that the algorithm was able to segment apples with different sizes and colours in an outdoor environment with natural lighting conditions, with a single model obtained from images captured using a monocular colour camera. The segmentation results are applied to the problem of fruit counting and the results are compared against manual counting. The results show a squared correlation coefficient of R2 = 0.81.

Threat awareness for critical infrastructures resilience <=SEP=> Utility networks are part of every nation's critical infrastructure, and their protection is now seen as a high priority objective. In this paper, we propose a threat awareness architecture for critical infrastructures, which we believe will raise security awareness and increase resilience in utility networks. We first describe an investigation of trends and threats that may impose security risks in utility networks. This was performed on the basis of a viewpoint approach that is capable of identifying technical and non-technical issues (e.g., behaviour of humans). The result of our analysis indicated that utility networks are affected strongly by technological trends, but that humans comprise an important threat to them. This provided evidence and confirmed that the protection of utility networks is a multi-variable problem, and thus, requires the examination of information stemming from various viewpoints of a network. In order to accomplish our objective, we propose a systematic threat awareness architecture in the context of a resilience strategy, which ultimately aims at providing and maintaining an acceptable level of security and safety in critical infrastructures. As a proof of concept, we demonstrate partially via a case study the application of the proposed threat awareness architecture, where we examine the potential impact of attacks in the context of social engineering in a European utility company.

A 3D human motion refinement method based on sparse motion bases selection <=SEP=> Motion capture (MOCAP) is an important technique that is widely used in many areas such as computer animation, film industry, physical training and so on. Even with professional MOCAP system, the missing marker problems always occur. Motion refinement is an essential preprocessing step for MOCAP data based applications. Although many existing approaches for motion refinement have been developed, it is still a challenging task due to the complexity and diversity of human motion. A data driven based motion refinement method is proposed in this paper, which modifies the traditional sparse coding process for special task of motion recovery from missing parts. Meanwhile, the objective function is derived by taking both statistical and kinematical property of motion data into account. Poselet model and moving window grouping are applied in the proposed method to achieve a fine-grained feature representation, which preserves the embedded spatial-temporal kinematic information. 5 motion dictionaries are learnt for each kind of poselet from training data in parallel. The motion refine problem is finally solved as an ℓ1-minimization problem. Compared with several state-of-art motion refine methods, the experimental result shows that our approach outperforms the competitors.

Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation <=SEP=> We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.

Omnidirectional conformal antenna array based on E-shaped patches <=SEP=> An antenna consisting of 4 element subarrays of strongly coupled E-shaped patches placed conformally on a metal cylinder is proposed for use in wireless local area networks (WLAN). The use of this special array configuration in the cylindrical case allows reaching a 8.3 % bandwidth and an omnidirectional radiation pattern in the horizontal plane with only 2 subarrays. CST Microwave Studio is used for validation purposes before manufacturing. To the knowledge of the authors, it is the first time that this recently introduced concept of strongly coupled E-shaped patches is used in a conformal antenna.

Recurrent Memory Array Structures Technical Report <=SEP=> The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves stateof-the-art performance on character level text prediction achieving 1.402 BPC† on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.

Information Fusion in the Immune System <=SEP=> Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.

Synthetic Aperture Radar Imaging Using Stepped Frequency Waveform <=SEP=> This paper presents a synthetic aperture radar (SAR) imaging system using a stepped frequency waveform. The main problem in stepped frequency SAR is the range difference in one sequence of pulses. This paper analyzes the influence of range difference in one sequence of pulses in detail and proposes a method to compensate this influence in the Doppler domain. A Stolt interpolation for the stepped frequency signal is used to focus the data accurately. The parameters for the stepped frequency SAR are analyzed, and some criteria are proposed for system design. Finally, simulation and experimental results are presented to demonstrate the validity of the proposed method. The experimental data are collected by using a stepped frequency radar mounted on a rail.

Follow me cloud: interworking federated clouds and distributed mobile networks <=SEP=> This article introduces the Follow-Me Cloud concept and proposes its framework. The proposed framework is aimed at smooth migration of all or only a required portion of an ongoing IP service between a data center and user equipment of a 3GPP mobile network to another optimal DC with no service disruption. The service migration and continuity is supported by replacing IP addressing with service identification. Indeed, an FMC service/application is identified, upon establishment, by a session/service ID, dynamically changing along with the service being delivered over the session; it consists of a unique identifier of UE within the 3GPP mobile network, an identifier of the cloud service, and dynamically changing characteristics of the cloud service. Service migration in FMC is triggered by change in the IP address of the UE due to a change of data anchor gateway in the mobile network, in turn due to UE mobility and/or for load balancing. An optimal DC is then selected based on the features of the new data anchor gateway. Smooth service migration and continuity are supported thanks to logic installed at UE and DCs that maps features of IP flows to the session/service ID.