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Nowadays, more and more activity recognition algorithms begin to improve recognition performance by combining the RGB and depth information. Although, the space-time volumes (STV) algorithm and the space-time local features algorithm can combine the RGB and depth information effectively, they also have their own defects. Such as they need expensive computational cost and they are not suitable for...
EEG signal is one of the most important signals for diagnosing some diseases. EEG is always recorded with an amount of noise, the more noise is recorded the less quality is the EEG signal. The included noise can represent the quality of the recorded EEG signal, this paper proposes a signal quality assessment method for EEG signal. The method generates an automated measure to detect the noise level...
Both consumer market and manufacturing industry makes heavy use of 1D (linear) barcodes. From helping the visually impaired to identifying the products to industrial automated industry management, barcodes are the prevalent source of item tracing technology. Because of this ubiquitous use, in recent years, many algorithms have been proposed targeting barcode decoding from high-accessibility devices...
Online handwriting recognition has many applications and the recognition with high accuracy is essential. In this paper, we introduce a method for online handwriting Farsi character and number recognition using Hidden Markov Models (HMM). First we recognize handwriting direction then we get some statistical and formatting features. The letters are classified by means of these features and then we...
Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the...
As neurobiological evidence points to the neocortex as the brain region mainly involved in high-level cognitive functions, an innovative model of neocortical information processing has been recently proposed. Based on a simplified model of a neocortical neuron, and inspired by experimental evidence of neocortical organisation, the Hierarchical Temporal Memory (HTM) model attempts at understanding...
Cloud based systems(CBSs) are increasing in the computing world. These systems derive their complexity due to both the disparate components and the diverse stake holders involved in them. The component wise security alone does not solve the problem of securing CBSs, but the stakeholder's computational space spanning across many components of the CBS, needs to be secured too. There have been initial...
In this paper we propose Fourier-Bessel cepstral coefficients (FBCC) features for robust speech recognition. The Fourier-Bessel representation of the speech signal is obtained using Bessel function as a basis set. The FBCC are extracted from zeroth order Bessel coefficients taking into account of the perceptual characteristics of human auditory system. Recognition accuracy is measured using the CMU...
In this paper we introduce a new cepstral coefficient extraction method based on an intelligibility measure for speech in noise, the Glimpse Proportion measure. This new method aims to increase the intelligibility of speech in noise by modifying the clean speech, and has applications in scenarios such as public announcement and car navigation systems. We first explain how the Glimpse Proportion measure...
Phenomena like filled pauses, laughter, breathing, hesitation, etc. play significant role in everyday human-to-human conversation and have a significant influence on speech recognition accuracy [1]. Because of their nature (e. g. long duration), they should be modeled with different number of emitting states and Gaussian mixtures. In this paper we address this question and try to determine the most...
By explicitly modelling the distortion of speech signals, model adaptation based on vector Taylor series (VTS) approaches have been shown to significantly improve the robustness of speech recognizers to environmental noise. However, the computational cost of VTS model adaptation (MVTS) methods hinders them from being widely used because they need to adapt all the HMM parameters for every utterance...
Eigenvoice and vector Taylor series (VTS) are good models for speaker differences and environmental variations separately. However, speaker and environmental variation always coexist in real-world speech. In this paper, we propose to combine eigenvoice and VTS. Specifically, we introduce eigenvoice speaker modeling for the clean speech into VTS's nonlinear mismatch function. In contrast, the standard...
This paper aims to evaluate the accuracy of optical character recognition (OCR) systems on real scanned books. The ground truth e-texts are obtained from the Project Gutenberg website and aligned with their corresponding OCR output using a fast recursive text alignment scheme (RETAS). First, unique words in the vocabulary of the book are aligned with unique words in the OCR output. This process is...
Hidden Markov models (HMMs) are widely employed in sequential data modeling both because they are capable of handling multivariate data of varying length, and because they capture the underlying hidden properties of time-series. Over the years, HMM-based clustering methods have been widely investigated and improved. However, their performance on noisy data and the effectiveness of similarity measure...
This paper tackles the problem of detecting the swinging action of an electronic handbell. It describes a threshold-based algorithm that is able to detect an orientation-free swinging motion using only the X and Y axis signals of an accelerometer that is mounted at the end of a handle. Equations governing the accelerations of the accelerometer are defined. The equations are used to select the appropriate...
Aspiration is an important phonemic feature in several Indian languages. Unlike English, languages such as Marathi have lexicons in which words with different meanings differ only in the aspiration feature of the initial voiced or unvoiced stop. Thus the reliable discrimination of aspirated stops from their unaspirated counterparts is important in automatic speech recognition for such languages. The...
In previous work we have shown that an ASR system consisting of a dual-input DBN which simultaneously observes MFCC acoustic features and predicted phone labels that are generated by an exemplar-based Sparse Classification (SC) system can achieve better word recognition accuracies in noise than a system observing only one of those input streams. This paper explores two modifications of the SC input...
A new loss function has been introduced for Minimum Classification Error, that approaches optimal Bayes' risk and also gives an improvement in performance over standard MCE systems when evaluated on the Aurora connected digits database.
In this paper auditory like features MLPC and MFCC have been used as front-end and their performance has been evaluated on Aurora-2 database for Hidden Markov Model (HMM) based noisy speech recognition. The clean data set is used for training and test set A is used to examine the performance. It has been found that almost the same recognition performance has been obtained both for MLPC and MFCC and...
This paper proposes a new robust speech recognition method. Since the hidden Markov model (HMM) algorithm need a lot of training calculation, The dynamic time warping (DTW) algorithm based on median filter is used instead in our system. According to the short-term energy method, the non-speech segment can be removed. Recognition accuracy is thus improved. The cepstral mean subtraction (CMS), running...
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