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Speech time series are manifolds in high dimensional feature space. The models of Speech recognition are to reflect the characteristics of the feature space distribution of time series manifolds. Each state in Hidden Markov Model (HMM) corresponds to an area in feature space, and the number of states in model corresponds to time sampling accuracy of manifolds in a higher level. From time and space...
This paper proposes a classification method of a stored motion-data. Robotic technology has made progress, and robots are demanded to cooperate with human. To realize the human and robot exist together, a motion recognition system is needed. In the conventional method, the stored motion-data is classified in advance to search the motion quickly and accurately. However, the task of the classification...
This paper considers the identification of kinetic parameters associated with the dynamics of closed biochemical reaction networks. These reaction networks are modeled by chemical master equations in which the reactions and the associated concentrations/populations of species are characterized by probability distributions. The vector of unknown kinetic parameters is usually highly sparse. Using this...
Recurrent neural network language models have solved the problems of data sparseness and dimensionality disaster which exist in traditional N-gram models. RNNLMs have recently demonstrated state-of-the-art performance in speech recognition, machine translation and other tasks. In this paper, we improve the model performance by providing contextual word vectors in association with RNNLMs. This method...
Facing diverse network attack strategies and overwhelming alters, much work has been devoted to correlate observed malicious events to pre-defined scenarios, attempting to deduce the attack plans based on expert models of how network attacks may transpire. Sophisticated attackers can, however, employ a number of obfuscation techniques to confuse the alert correlation engine or classifier. Recognizing...
We investigate communicative strategies used by humans in negotiation dialogues. The empirical material of the study is a small sub-corpus of telemarketing calls. Our aim is to develop software for automatic pragmatic analysis of dialogues which can be used by linguists for recognition of dialogue acts, communicative strategies and the structure of a dialogue. The further aim is to implement the results...
Risk estimation for the current traffic situation is crucial for safe autonomous driving systems. One part of the uncertainty in risk estimation is the behavior of the surrounding traffic participants. In this paper we focus on highway scenarios, where possible behaviors consist of a change in acceleration and lane change maneuvers. We present a novel approach for the recognition of lane change intentions...
Knowing channel sight condition is important as it has a great impact on localization performance. In this paper, a RSS-based localization algorithm, which jointly takes into consideration the effect of channel sight conditions, is investigated. In our approach, the channel sight conditions experience by a moving target to all sensors is modeled as a hidden Markov model (HMM), with the quantized measured...
In this paper we proposed a case-based reasoning (CBR) mechanism with improved vector space model. In order to get more accuracy of case retrieval, this paper presents the improved algorithm of the text feature-weight calculation and Chinese word order calculation. And a supermarket guiding robot is designed to verify the reasoning machine as an example. The experiments show that compared with the...
A recently proposed concept for training reverberation-robust acoustic models for automatic speech recognition using pairs of clean and reverberant data is extended from word models to tied-state triphone models in this paper. The key idea of the concept, termed ICEWIND, is to use the clean data for the temporal alignment and the reverberant data for the estimation of the emission densities. Experiments...
Gaussian Processes (GPs) are gaining increasing popularity due to their expressive power for learning the dynamics of non-linear time series data, e.g. for human motion prediction. However, so far they are restricted to Euclidean space: input data such as position and velocity need to be Euclidean. In this paper, we examine GPs over time series of 6D rigid body motions including large rotations. As...
This paper presents a novel robot pose measure for human movement imitation based entirely on the Euclidean distance information between any two links of a robot and any link and object in the robot's environment in a Cartesian task space. A Hidden Markov Model is used to encode the spatio-temporal information of multiple demonstrations. In combination with Gaussian Mixture Regression for extracting...
To engage in cooperative activities with human partners, robots have to possess basic interactive abilities and skills. However, programming such interactive skills is a challenging task, as each interaction partner can have different timing or an alternative way of executing movements. In this paper, we propose to learn interaction skills by observing how two humans engage in a similar task. To this...
In this work, we present a methodology for enabling a robot to identify an object and grasp configuration of interest and assist the human teleoperating the robot, to grasp the object. The identification is carried out in real-time by detecting the motion intention of the human as they are teleoperating the remote robotic arm towards the object and the grasp configuration. Simultaneously, depending...
We present a technique to classify human actions that involve object manipulation. Our focus is to accurately distinguish between actions that are related in that the object's state changes define the essential differences. Our algorithm uses a latent variable conditional random field that allows for the modelling of spatio-temporal relationships between the human motion and the corresponding object...
This paper reports the results of a system based on hidden Markov models (HMM) that is used to interpret both static and dynamic divers' hand signals using real time video feed. Two methods of collecting features that describe diver gestures are described and two types of HMMs are investigated: one based on discrete outputs variable distribution and the other based on mixture of Gaussians outputs...
Because the basis of students' math and limited time, the voice recognition is facing a dilemma. This article contact teaching practice. The content of the article is divided into two types, one is voice recognition principle, the other is speech recognition practice, We propose two types of the weakening formula derivation and stressed that the process of practice teaching ideas. In order to improve...
High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here,...
In this work, we present a reliable and continuous gesture recognition method that supports a natural and flexible interaction between the human and the robot. The aim is to provide a system that can be trained online with few samples and can cope with intra user variability during the gesture execution. The proposed approach relies on the generation of an ad-hoc Hidden Markov Model (HMM) for each...
A model is proposed to developed a Indigenous language (Galo) sentence's pitch-contour with sentence-wide optimization, called the sentence pitch-contour using HMM(Hidden Markov Model) & VQ (vector quantization). To develop a sentence pitch-contour (SPC-HMM), each training sentence are normalized for the pitch-contours of the syllables. Our model is effective for pitch height normalization...
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