Classification of coarse phonetic categories in continuous speech: statistical classifiers vs. temporal flow connectionist network

2002 ◽  
Author(s):  
A. Aktas ◽  
O. Schmidbauer ◽  
K.H. Maier ◽  
W.H. Feix
Keyword(s):  
Continuous Speech ◽  
Connectionist Network ◽  
Statistical Classifiers ◽  
Phonetic Categories ◽  
Temporal Flow

scholarly journals Multimodal Object Classification Models Inspired by Multisensory Integration in the Brain

2019 ◽  
Vol 9 (1) ◽  
pp. 3 ◽  
Author(s):  
Rajesh Amerineni ◽  
Resh S. Gupta ◽  
Lalit Gupta
Keyword(s):  
Multisensory Integration ◽  
Object Classification ◽  
Classification Systems ◽  
Multilayer Perceptrons ◽  
Classification Models ◽  
Multivariate Statistical ◽  
Statistical Classifiers ◽  
Multimodal Classification ◽  
The Brain

Two multimodal classification models aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli are introduced. The feature-integrating model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The decision-integrating model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions.

Knowledge-Based Features for Place Classification of Unvoiced Stops

2013 ◽  
Vol 22 (3) ◽  
pp. 215-228
Author(s):  
Veena Karjigi ◽  
Preeti Rao
Keyword(s):  
Speech Production ◽  
Speech Signal ◽  
Continuous Speech ◽  
Tracking Method ◽  
Band Energy ◽  
Knowledge Based ◽  
Formant Tracking ◽  
Context Features ◽  
Better Than

AbstractThe classification of unvoiced stops in consonant–vowel (CV) syllables, segmented from continuous speech, is investigated by features related to speech production. As burst and vocalic transitions contribute to identification of stops in the CV context, features are computed from both regions. Although formants are the truly discriminating articulatory features, their estimation from the speech signal is a challenge especially in unvoiced regions like the release burst of stops. This may be compensated partially by sub-band energy-based features. In this work, formant features from the vocalic region are combined with features from the burst region comprising sub-band energies, as well as features from a formant tracking method developed for unvoiced regions. The overall combination of features at the classifier level obtains an accuracy of 84.4%, which is significantly better than that obtained with solely sub-band features on unvoiced stops in CV syllables of TIMIT.

scholarly journals Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7718
Author(s):  
Olaf Bar ◽  
Łukasz Bibrzycki ◽  
Michał Niedźwiecki ◽  
Marcin Piekarczyk ◽  
Krzysztof Rzecki ◽  
...  
Keyword(s):  
Recognition Accuracy ◽  
Cosmic Ray ◽  
Cmos Technology ◽  
Zernike Moment ◽  
Geometrical Properties ◽  
Statistical Classifiers ◽  
Image Function ◽  
Feature Based ◽  
Neural Network Classifiers

Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.

Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification

2012 ◽  
pp. 154-167 ◽  
Author(s):  
D. Jude Hemanth ◽  
D. Selvathi ◽  
J. Anitha
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Classification Accuracy ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Statistical Classifiers ◽  
Mr Brain ◽  
Abnormal Brain

In the present study, the effectiveness of the adaptive resonance theory neural network (ART2) is illustrated in the context of automatic classification of abnormal brain tumor images. Abnormal images from four different classes namely metastase, meningioma, glioma and astrocytoma have been used in this work. Initially, textural features are extracted from these images. An extensive feature selection is performed to optimize the number of features. These optimized features are then used to classify the images using ART2 neural network. Experimental results show promising results for the ART2 network in terms of classification accuracy and convergence rate. A comparison is made with other conventional classifiers to show the superior nature of ART2 neural network. The classification accuracy of the ART2 classifier is significantly higher than the statistical classifiers. ART2 classifier is also computationally feasible over other neural classifiers. Thus this work suggests ART2 neural network as an optimal image classifier which finds application in clinical field.

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