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This paper investigates the use of the speech parameter generation (SPG) algorithm, which has been successfully adopted in deep neural network (DNN)-based voice conversion (VC) and speech synthesis (SS), for incorporating temporal information to improve the deep denoising auto-encoder (DDAE)-based speech enhancement. In our previous studies, we have confirmed that DDAE could effectively suppress noise...
We introduce system theoretic notions of a Hankel operator, and Hankel norm for hidden Markov models. We show how the related Hankel singular values provide lower bounds on the norm of the difference between a hidden Markov model of order n and any lower order approximant of order n̂ < n.
Many human activities, given their intrinsic modularity, present structural information which can be exploited by classification algorithms: this enhances the capability of robots to predict activities. We introduce a semantic reasoning paradigm in which, via logical and statistical learning, we discriminate between actions on the basis of contextual associations. An example of this is considering...
In this paper we propose a Hidden Markov Model for modeling and extracting vine structure from images. We built up from previous research to infer connectivity of cane segments extracted from binary images. We use skeletonisation and polylines to model cane segments and we use simulated annealing to optimize an energy function defined in terms of attributes observed for each connection. We formulate...
Pedestrian-to-vehicle communication is an effective method to reducing pedestrian accidents, but its performance is greatly degraded when many pedestrians contend to transmit frequently on the same channel. In this paper, we propose to solve this problem from three aspects, (i) defining accident models for intersections and straight roads where pedestrian accidents frequently occur, (ii) estimating...
This paper presents application of deep learning to recognize online handwritten mathematical symbols. Recently various deep learning architectures such as Convolution neural network (CNN), Deep neural network (DNN) and Long short term memory (LSTM) RNN have been applied to fields such as computer vision, speech recognition and natural language processing where they have been shown to produce state-of-the-art...
Automatic video analysis of interactions between road users is desired for city and road planning. A first step of such a system is object localization of road users. In this work, we present a method of detecting a specific car in an intersection from a monocular camera image. A camera calibration and segmentation are utilized as inputs by the method in order to detect a car. Using these inputs,...
The pervasiveness of location-acquisition technologies enable location-based social networks (LBSN) to become increasingly popular in recent years. Users are able to check-in their current location and share information with other users through these networks. LBSN check-in data can be used for the benefit of users by providing personalized recommendations. There are several location recommendation...
With information processing and retrieval of spoken documents becoming an important topic, there is a need of systems performing automatic segmentation of audio streams. Among such algorithms, spoken term discovery allows the extraction of word-like units (terms) directly from the continuous speech signal, in an unsupervised manner and without any knowledge of the language at hand. Since the performance...
In the last decades, education has been supported by technology in order to reach more people and produce significant benefits to teaching and learning. Part of the success of technology and education is due to the capacity technology can adapt teaching and learning to different contexts. More recently, we noticed an increase in the use of game elements (gamification) in educational environments,...
With development of various sensors attached to mobile and wearable devices, recognizing user's current context and giving an appropriate service come to hot issue. In this paper, we propose the context-aware system recognizing user's dining context that can occur within a great variety of contexts. The model uses low-level sensor data from mobile and wristwearable devices that can be widely available...
Given a context free language G over alphabet &#x03A3; and a string s, the language edit distance problem seeks the minimum number of edits (insertions, deletions and substitutions) required to convert s into a valid member of the language L(G). The well-known dynamic programming algorithm solves this problem in cubic time in string length [Aho, Peterson 1972, Myers 1985]. Despite its numerous...
In this paper a semantic-probabilistic network for event recognition is proposed. The approach uses pre-defined domain ontology to describe the events and scenarios in the scene as a hierarchical decomposition of simple concepts and variables and then perform an automated conversion of the ontology into a Bayesian network. A novel approach to Bayesian network nodes weights calculation is used based...
Human action recognition is a challenging task not only because of the factors like changes in intensity, background, etc but also because of the variability in the behavioural patterns among the objects in the image which in turn affects the recognition accuracy. Analyzing all those factors and identifying the action is termed as activity recognition. In this paper, we present an approach of activity...
Providing accurate/suitable information on behaviors in smart environments is a challenging and crucial task in pervasive computing where context-awareness and pro-activity are of fundamental importance. Behavioral identifications enable to abstract higher-level concepts that are interesting to applications. This work proposes the unified logical-based framework to recognize and analyze behavioral...
Smart home development has many methods that cause the rise of various smart home systems which has its own uniqueness on every system. Nevertheless, those systems could not accommodate their communication and information sharing. It causes the difficulties of collaboration on a system to another, and it makes hard to implement an existing models on a new system. One of solution to answer this problem...
Vowel durations are most often utilized in studies addressing specific issues in phonetics. Thus far this has been hampered by a reliance on subjective, labor-intensive manual annotation. Our goal is to build an algorithm for automatic accurate measurement of vowel duration, where the input to the algorithm is a speech segment contains one vowel preceded and followed by consonants (CVC). Our algorithm...
Human emotional expression tends to evolve in a structured manner in the sense that certain emotional evolution patterns, i.e., anger to anger, are more probable than others, e.g., anger to happiness. Furthermore the perception of an emotional display can be affected by recent emotional displays. Therefore, the emotional content of past and future observations could offer relevant temporal context...
The expressivity of virtual, animated agents plays an important role in their believability. While the planning and goal-oriented aspects of agent movements have been addressed in the literature extensively, expressing the emotional state of the agents in their movements is an open research problem. We present our interactive animated agent model with controllable affective movements. We have recorded...
We present a method for segmenting a set of unstructured demonstration trajectories to discover reusable skills using inverse reinforcement learning (IRL). Each skill is characterised by a latent reward function which the demonstrator is assumed to be optimizing. The skill boundaries and the number of skills making up each demonstration are unknown. We use a Bayesian nonparametric approach to propose...
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