Meta-Classifiers in Acoustic and Linguistic Feature Fusion-Based Affect Recognition

Schuller, Björn; Villar, R.J.; Rigoll, Gerhard; Lang, M.

doi:10.1109/icassp.2005.1415116

Cited by 50 publications

(42 citation statements)

References 4 publications

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“…Previous work [2] has indicated that an average emotion recognition rate of 84% is achieved in speaker-dependent experiments, whereas for the speaker-independent case the emotion recognition drops to 60%. The aforementioned conclusion is also verified in [56] for 10 different classifiers. The averaged emotion recognition performance equals 89.49% for the speakerdependent case, whereas it drops to 71.29% for the speaker-independent one.…”

Section: Speaker-independent Experimental Protocolsupporting

confidence: 61%

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Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Kotti

Paternò

2012

Int J Speech Technol

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In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisaki's model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers' error rates and then to evaluate the information expressed by the classifiers' confusion matrices.

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Section: Speaker-independent Experimental Protocolsupporting

confidence: 61%

“…75 features were found to have the best performance (emotion recognition accuracy equal to 97.0%) for the linear SVM. This procure has been verified to be successful in [56]. For reasons of homogeneity the same number of features is retained for the remaining levels of the psychologicallyinspired binary cascade classification schema.…”

Section: Feature Selectionmentioning

confidence: 99%

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Kotti

Paternò

2012

Int J Speech Technol

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“…If confidences are provided on lower level, they can be exploited as well. Still, the gain over single strong classifiers such as SVM may not justify the extra computational costs [178].…”

Section: Classificationmentioning

confidence: 99%

“…Ensembles of classifiers [183,184,178,130,129] combine their individual strengths, and might improve training stability. There exists a number of different approaches to combine classifiers.…”

Section: Classificationmentioning

confidence: 99%

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Schuller

Batliner

Steidl

et al. 2011

Speech Communication

605

329

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More than a decade has passed since research on automatic recognition of emotion from speech has become a new field of research in line with its 'big brothers' speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In a first part, we address the basic phenomenon reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then shift to automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the first comparative challenge on emotion recognition from speech -the INTERSPEECH 2009 Emotion Challenge, organised by (part of) the authors, including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the actual learnt lessons before we finally address the ever-lasting problems and future promising attempts.Keywords: emotion, affect, automatic classification, feature types, feature selection, noise robustness, adaptation, standardisation, usability, evaluation Setting the SceneThis special issue will address new approaches towards dealing with the processing of realistic emotions in speech, and this overview article will give an account of the state-of-the-art, of the lacunas in this field, and of promising approaches towards overcoming shortcomings in modelling and recognising realistic emotions. We will also report on the first emotion challenge at INTERSPEECH 2009, constituting the initial impetus of this special issue; to end with, we want to sketch future strategies and applications, trying to answer the question 'Where to go from here?'The article is structured as follows: we first deal with the basic phenomenon briefly reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then proceed to automatic processing (sec. 2) including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the the first Emotion Challenge (sec. 3) including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the lessons learnt, before concluding this article (sec. 4).

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“…Модель ELM недавно была успешно использована для задачи многомодального (аудиовизуального) распознавания эмоций в реальных условиях (in the wild) [14]. В недавней обзорной статье также отмечалось [5], что некоторые методы, устоявшиеся в области машинного обучения [15], не очень обдуманно применяются к задаче РЭР, например, объединение множественных моделей на уровне позднего объединения информации. [16]) по двум конкурсам/направлениям (sub-challenges): наиболее точная классификация эмоций в речи (Classifier Performance Sub-Challenge) [17] (здесь и далее приведены ссылки на статьи, описывающие системы, победившие в каждом из кон-курсов) и конкурс открытых проектов по автоматическому паралингвистическому анализу речи (Open Performance Sub-Challenge) [18].…”

Section: архитектура базовой системы паралингвистического анализа речиunclassified

State-of-the-art tasks and achievements of paralinguistic speech analysis systems

Karpov¹,

Kaya²,

Salah³

2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Meta-Classifiers in Acoustic and Linguistic Feature Fusion-Based Affect Recognition

Cited by 50 publications

References 4 publications

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

State-of-the-art tasks and achievements of paralinguistic speech analysis systems

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