scholarly journals Comparative Study of Multi-stage Classification Scheme for Recognition of Lithuanian Speech Emotions

2016 ◽  
Author(s):  
Tatjana Liogiene ◽  
Gintautas Tamulevičius
Keyword(s):  
Comparative Study ◽  
Classification Scheme ◽  
Multi Stage ◽  
Stage Classification

scholarly journals Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

2016 ◽  
Vol 10 (1) ◽  
pp. 35-41 ◽  
Author(s):  
Tatjana Liogienė ◽  
Gintautas Tamulevičius
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Classification Scheme ◽  
Recognition Rate ◽  
Single Stage ◽  
Speech Emotion Recognition ◽  
Forward Selection ◽  
Multi Stage ◽  
Stage Scheme ◽  
Stage Classification

Abstract The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

Comparative Study of Cemented Versus Uncemented Multi-Stage Fractured Wells in the Barnett Shale

2010 ◽  
Author(s):  
Darrell Stephen Lohoefer ◽  
Daniel Snyder ◽  
Rocky Allen Seale ◽  
Daniel Jon Themig
Keyword(s):  
Comparative Study ◽  
Barnett Shale ◽  
Multi Stage

A Multi-stage Classification Framework for Disaster-Specific Tweets

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Harshit Mehrotra ◽  
Akanksha Mishra ◽  
Sukomal Pal
Keyword(s):  
Classification Framework ◽  
Multi Stage ◽  
Stage Classification

HEp-2 Cell Classification Using Descriptors Fused into the Dissimilarity Space into the Dissimilarity Space

2014 ◽  
Vol 23 (03) ◽  
pp. 1460006
Author(s):  
Ilias Theodorakopoulos ◽  
Dimitris Kastaniotis ◽  
George Economou ◽  
Spiros Fotopoulos
Keyword(s):  
Autoimmune Diseases ◽  
Classification Scheme ◽  
State Of The Art ◽  
Svm Classifier ◽  
Cell Level ◽  
Rotation Invariant ◽  
Classification Rate ◽  
Stage Classification ◽  
Linear Svm ◽  
Serum Detection

Autoimmune diseases are strictly connected with the presence of autoantibodies in patient serum. Detection of Antinucleolar Antibodies (ANAs) in patient serum is performed using a laboratory technique named Indirect Immunofluorescence (IIF) followed by manual evaluation on the acquired slides from specialized personnel. In this procedure, several limitations appear and several automatic techniques have been proposed for the task of ANA detection. In this work we present a method achieving state-of-the-art performance on a publicly available dataset. More precisely, two powerful and rotation invariant descriptors are incorporated into a two stage classification scheme where the feature vectors are represented and fused in the dissimilarity space. Then, in a second level dissimilarity vectors are classified using a linear SVM classifier. Evaluation on the HEp-2 cell contest dataset yields a 70.16% performance on cell-level classification. Furthermore we provide results in Image Level Classification where a 78.57% classification rate was achieved.

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