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In speech recognition system, an improved multi-base neural network speech recognition model is proposed to solve the problem of long learning time and slow convergence rate of deep neural network. However, the improved model introduces a large number of parameters in the training process to make the model over-fitted in the test set, resulting in the deterioration of generalization ability and the...
In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti...
This paper presents a review on few notable speech recognition models that are reported in the last decade. Firstly, the models are categorized into sparse models, learning models and domain - specific models. Subsequently, the characteristics of the models have been observed using speech constraints, algorithmic constraints and performance constraints. The performance of these models reported in...
The demand of human identification in a non-intrusive manner has risen increasingly in recent years. Several works have already been done in this context using gait-cycle detection from human skeleton data using Microsoft Kinect as a data capture sensor. In this paper we have proposed a novel method for automatic human identification in real time using the fusion of both supervised and unsupervised...
In this paper we propose an improvement of a human action recognition method that uses a string-based representation and a string edit distance to compare the observed action with reference actions in the training set. In particular, the original improvement is based on a specific formulation of the string edit distance that is more suited to take into account the problems related to noise and to...
Anomaly detection systems rely on machine learning techniques to model the normal behavior of the system. This model is used during operation to detect anomalies due to attacks or design faults. Ensemble methods have been used to improve the overall detection accuracy by combining the outputs of several accurate and diverse models. Existing Boolean combination techniques either require an exponential...
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. It will be a daunting task for system administrators to manually keep track of the execution status of a large number of virtual machines all the time. Anomaly prediction is an effective approach to enhancing availability and reliability of Cloud infrastructures...
This paper introduces a novel approach to predict human motion for the Non-binding Lower Extremity Exoskeleton (NBLEX). Most of the exoskeletons must be attached to the pilot, which exists potential security problems. In order to solve these problems, the NBLEX is studied and designed to free pilots from the exoskeletons. Rather than applying Electromyography (EMG) and Ground Reaction Force (GFR)...
Parts of speech tagging is an important research topic in Natural Language Processing research are. Since it is one among the first steps of any natural language processing (NLP) techniques such as machine translation, if any error happens for tagging the same will repeat in the whole NLP process. So far works had been done on POS tagging based on SVM, MBLP, HMM, Ngram. All of these methods were not...
Sub-word units like morphemes are selected as the lexicon for highly inflectional languages, as they can provide better coverage and a smaller vocabulary size. However, short units shrink the context of statistical models, prone to morpho-phonetic changes, and not always outperform the word based model. When sequence of units are merged or split, unit boundaries are phonetically harmonized in the...
This paper introduces a new back-end classifier for a speech recognition system that is based on artificial life (ALife). The ALife species being used for classification purposes are called wains, which were developed using the Créatúr framework. The speech recognition task used in the evaluation of the new classifier is that of isolated digit recognition. Performance of the proposed back-end classifier...
Video-based coaching systems have seen increasing adoption in various applications including dance, sports, and surgery training. Most existing systems are either passive (for data capture only) or barely active (with limited automated feedback to a trainee). In this paper, we present a video-based skill coaching system for simulation-based surgical training by exploring a newly proposed problem of...
With the development of sensing equipments, data from different modalities is available for gesture recognition. In this paper, we propose a novel multi-modal learning framework. A coupled hidden Markov model (CHMM) is employed to discover the correlation and complementary information across different modalities. In this framework, we use two configurations: one is multi-modal learning and multi-modal...
High false alarm rates and execution times are among the key issues in host-based anomaly detection systems. In this paper, we investigate the use of trace abstraction techniques for reducing the execution time of anomaly detectors while keeping the same accuracy. The key idea is to represent system call traces as traces of kernel module interactions and use the resulting abstract traces as input...
Support Vector Machines (SVM) are applied to the problem of detecting and classifying broad acoustic-phonetic classes (events). In this paper an approach based on Non-Negative Matrix Deconvolution (NMD) is proposed to merge frame-based SVM predictions into segmental events. To turn the SVM outputs, which are frame-based, into a signal segmented in terms of events, two different event merger methods...
This paper presents a system for automatic bird identification, which uses audio input. The experiments have been conducted on three groups of birds, which were created basing finishing on classification, the system is fully automated. The main problem in automatic bird recognition (ABR) is the choice of proper features and classifiers. Identification has been made using two classifiers-kNN (k Nearest...
We propose the prediction-adaptation-correction RNN (PAC-RNN), in which a correction DNN estimates the state posterior probability based on both the current frame and the prediction made on the past frames by a prediction DNN. The result from the main DNN is fed back to the prediction DNN to make better predictions for the future frames. In the PAC-RNN, we can consider that, given the new, current...
Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from...
In comparison with standard HMM (Hidden Markov Model) with forced alignment, this paper discusses two automatic segmentation algorithms from different points of view: the probabilities of insertion and omission, and the accuracy. The first algorithm, hereafter named the refined HMM algorithm, aims at refining the segmentation performed by standard HMM via a GMM (Gaussian Mixture Model) of each boundary...
Video content classification and retrieval is a necessary tool in the current merging of entertainment and information media. With the advent of broadband networking, every consumer will have video programs available on-line as well as in the traditional distribution channels. Systems that help in content management have to discern between different categories of video in order to provide for fast...
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