Discrimination Between Native and Non-Native Speech Using Visual Features Only

Georgakis, Christos; Petridis, Stavros; Pantić, Maja

doi:10.1109/tcyb.2015.2488592

Cited by 8 publications

(3 citation statements)

References 55 publications

(73 reference statements)

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“…This is exactly as expected; while shape features are capable of capturing coarse deformations related to facial expression, appearance features are efficient in encapsulating finer movements and tale-telling transient features such as bulges, wrinkles and furrows [8,74,44]. Also, SIFT outperforms DCT.…”

Section: Accepted M Manuscriptsupporting

confidence: 70%

“…As a matter of fact, the latter have been shown to outperform uni-modal frameworks in various related tasks such as continuous interest prediction [40,16], detection of behavioral mimicry [41], and dimensional and continuous affect prediction [39], to mention but a few. Notably, other challenging problems such as accent classification [42,43,44] and pain intensity estimation [45] have been addressed based exclusively on visual features.…”

Section: Featuresmentioning

confidence: 99%

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The Conflict Escalation Resolution (CONFER) Database

Georgakis

Panagakis

Zafeiriou

et al. 2017

Image and Vision Computing

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Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audio-visual recordings of televised political debates where conflicts naturally arise, and b) reporting baseline experiments on audio-visual conflict intensity estimation. The database contains approximately 142 minutes of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject-and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict clas- * Corresponding author. E-mail address: christos.georgakis@imperial.ac.uk Preprint submitted to Image and Vision Computing December 20, 2016 A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTsification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/.

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Section: Accepted M Manuscriptsupporting

confidence: 70%

Section: Featuresmentioning

confidence: 99%

The Conflict Escalation Resolution (CONFER) Database

Georgakis

Panagakis

Zafeiriou

et al. 2017

Image and Vision Computing

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“…(26) The respective closed-form solutions are obtained by substituting (25) and (26) into (23) or (24).…”

Section: Discussionmentioning

confidence: 99%

Discriminant Incoherent Component Analysis

Georgakis

Panagakis

Pantić

2016

IEEE Trans. on Image Process.

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Abstract-Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and action unit activations. In this paper, the Discriminant Incoherent Component Analysis (DICA) is proposed in order to extract lowcomplexity components corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and 1-norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and action unit detection by conducting experiments on 4 datasets. The proposed method outperforms all the methods that is compared to in all tasks and experimental settings.

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An Algorithm to Identify Syllable from a Visual Speech Recognition System

Subhashini

Kumar

2019

Wireless Pers Commun

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Discrimination Between Native and Non-Native Speech Using Visual Features Only

Cited by 8 publications

References 55 publications

The Conflict Escalation Resolution (CONFER) Database

The Conflict Escalation Resolution (CONFER) Database

Discriminant Incoherent Component Analysis

An Algorithm to Identify Syllable from a Visual Speech Recognition System

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