On the Necessity and Feasibility of Detecting a Driver’s Emotional State While Driving

Grimm, Michael; Kroschel, Kristian; Harris, Helen; Nass, Clifford; Schuller, Björn W.; Rigoll, Gerhard; Moosmayr, Tobias

doi:10.1007/978-3-540-74889-2_12

Cited by 77 publications

(56 citation statements)

References 15 publications

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“…Historically, classification into a limited number of (few) discrete classes came first [114]. With the advent of databases annotated in the dimensional space, regression was found as an alternative [83]. As real-life application is not limited to prototypical cases (cf.…”

Section: Classificationmentioning

confidence: 99%

“…First attempts to cope with noise were seen rather recently [121,171,245,83,191] and are characterised by the simplification of additive stationary noise. This does of course not take into account the alteration of voice characteristics of the speaker herself, speaking in noisy environment -the well-known Lombard effect, which is, however, covered to some extent by parts of the SUSAS database.…”

Section: Robustnessmentioning

confidence: 99%

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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

Self Cite

606

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: Classificationmentioning

confidence: 99%

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

Self Cite

606

329

View full text Add to dashboard Cite

show abstract

“…The experimental and commercial implementations often consist of multiple devices that contribute to the goal of valid and reliable evaluation of drivers' state, and, to some extent, are capable of estimating up to four driver state constructs: (i) cognitive distraction, (ii) mental workload, (iii) mental fatigue, and (iv) emotions (see TABLE I). These driver state constructs were found to influence driving performance both positively and negatively [2]- [4], [7]- [12]. Some of those were found to impair driving, for example high level of mental fatigue was found to be responsible for up to 20-30% of road fatalities [13].…”

Section: Driver State Monitoringmentioning

confidence: 99%

Towards hybrid driver state monitoring: Review, future perspectives and the role of consumer electronics

Melnicuk

Birrell

Crundall

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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“…Most of the existing efforts in this direction aim at the recognition of a subset of basic emotions from speech signals. In recent years, however, few studies have been made focusing on interpreting speech signals in terms of certain application-dependent affective states [9]- [12].…”

Section: Related Research and Motivationmentioning

confidence: 99%

Speech Emotion Analysis: Exploring the Role of Context

Tawari

Trivedi

2010

IEEE Trans. Multimedia

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Abstract-Automated analysis of human affective behavior has attracted increasing attention in recent years. With the research shift toward spontaneous behavior, many challenges have come to surface ranging from database collection strategies to the use of new feature sets (e.g., lexical cues apart from prosodic features). Use of contextual information, however, is rarely addressed in the field of affect expression recognition, yet it is evident that affect recognition by human is largely influenced by the context information. Our contribution in this paper is threefold. First, we introduce a novel set of features based on cepstrum analysis of pitch and intensity contours. We evaluate the usefulness of these features on two different databases: Berlin Database of emotional speech (EMO-DB) and locally collected audiovisual database in car settings (CVRRCar-AVDB). The overall recognition accuracy achieved for seven emotions in the EMO-DB database is over 84% and over 87% for three emotion classes in CVRRCar-AVDB. This is based on tenfold stratified cross validation. Second, we introduce the collection of a new audiovisual database in an automobile setting (CVRRCar-AVDB). In this current study, we only use the audio channel of the database. Third, we systematically analyze the effects of different contexts on two different databases. We present context analysis of subject and text based on speaker/text-dependent/-independent analysis on EMO-DB. Furthermore, we perform context analysis based on gender information on EMO-DB and CVRRCar-AVDB. The results based on these analyses are promising.

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On the Necessity and Feasibility of Detecting a Driver’s Emotional State While Driving

Cited by 77 publications

References 15 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

Towards hybrid driver state monitoring: Review, future perspectives and the role of consumer electronics

Speech Emotion Analysis: Exploring the Role of Context

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