Real-time vocal emotion recognition in artistic installations and interactive storytelling: Experiences and lessons learnt from CALLAS and IRIS

Vogt, Thurid; André, Elisabeth; Wagner, Johannes; Gilroy, Steve; Charles, Fred; Cavazza, Marc

doi:10.1109/acii.2009.5349501

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…In this respect also distributed emotion recognition could be a future topic, because with new technical requirements, a new issue will get more and more important: Real-time ability, which has been addressed only rarely so far, e. g., in [229]. However, some small, e. g., wearable devices might not provide sufficient computational power.…”

Section: Robustnessmentioning

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

614

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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Section: Robustnessmentioning

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

614

329

View full text Add to dashboard Cite

show abstract

“…With this in view, we have also talked about the graphical front-end for SSI that lets novice users train their own model. The source code of the SSI framework and the GUI tools are freely available under LGPL 7 .…”

Section: Discussionmentioning

confidence: 99%

“…In the best case the training data will be recorded from users interacting with the actual application. However, our experiences have shown that this is often too time consuming, especially since it requires thorough annotation by experts [7]. Instead, application developers should be provided with a tool that allows them to create databases on their own, preferably in a simple and fast fashion.…”

Section: Model Trainingmentioning

confidence: 99%

The social signal interpretation framework (SSI) for real time signal processing and recognition

Wagner

Lingenfelser²,

André³

2011

Interspeech 2011

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“…[35]. One of the most famous appraisal models is the so-called OCC (Ortony, Clore, and Collins [12]) model and a very common dimensional model is Russel's circumplex model of affect [25], which we aim to use in this work because it allows us to cope with the diversity of emotions by mapping emotions to discrete emotion classes, such as positive/higharousal, positive/low-arousal, neutral, neutral/high-arousal, neutral/low-arousal [36], instead of defining fix emotions.…”

Section: B Emotion Recognitionmentioning

confidence: 99%

Emotion Recognition Based Preference Modelling in Argumentative Dialogue Systems

Rach

Weber

Aicher

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Within this work, we present an approach to model the opinion of a human towards a specific topic in a finegrained way by using weighted bipolar argumentation graphs. In addition, we discuss how the therefore required rating of related aspects can be collected by means of emotion recognition techniques and discuss an application scenario based on the state-of-the-art Argumentative Dialogue System EVA in which the proposed techniques can be applied.

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Real-time vocal emotion recognition in artistic installations and interactive storytelling: Experiences and lessons learnt from CALLAS and IRIS

Cited by 8 publications

References 9 publications

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

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

The social signal interpretation framework (SSI) for real time signal processing and recognition

Emotion Recognition Based Preference Modelling in Argumentative Dialogue Systems

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