Speech-based recognition of self-reported and observed emotion in a dimensional space

Truong, Khiet P.; Leeuwen, David A. van; Jong, Franciska de

doi:10.1016/j.specom.2012.04.006

Cited by 49 publications

(24 citation statements)

References 53 publications

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“…Cowie et al (2010) pointed out that challenges in using emotion labels are not only limited to ensuring that the labels are correct, but also that the raters agree on those labels. It has also been reported that perceived and actual states can be rather divergent (Tcherkassof et al, 2007), and the same goes for perceived and self-reported states (Truong et al, 2012).…”

Section: Modeling Affect By Predicting User Satisfactionmentioning

confidence: 77%

A satisfaction-based model for affect recognition from conversational features in spoken dialog systems

Lutfi

Fernández-Martínez

Lucas-Cuesta

et al. 2013

Speech Communication

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Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labeling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator's data, suggesting that user bias does exist in a laboratory-led evaluation.

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Section: Modeling Affect By Predicting User Satisfactionmentioning

confidence: 77%

A satisfaction-based model for affect recognition from conversational features in spoken dialog systems

Lutfi

Fernández-Martínez

Lucas-Cuesta

et al. 2013

Speech Communication

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“…However, a critical issue noted with respect to naturalistic materials is that even a large speech corpus contains few emotional samples [10]. To solve this issue, a novel paradigm was proposed to record naturalistic expression that contains the spontaneous emotions of speakers using the games and its effectiveness was demonstrated [11,12]. The present study adopted the game task with reference to the paradigm of previous studies [11,12].…”

Section: Methodsmentioning

confidence: 99%

“…To solve this issue, a novel paradigm was proposed to record naturalistic expression that contains the spontaneous emotions of speakers using the games and its effectiveness was demonstrated [11,12]. The present study adopted the game task with reference to the paradigm of previous studies [11,12]. Using the game task, the speakers talked with their friends with more naturally expressed emotion than they did when using another task [11].…”

Section: Methodsmentioning

confidence: 99%

Mutual emotional understanding in a face-to-face communication environment: How speakers understand and react to listeners' emotion in a game task dialog

Arimoto

Okanoya

2015

Acoust. Sci. & Tech.

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“…Additionally, these methods use classifiers with different schemes, some of them are K-nearest neighbor, Gaussian mixture models, hidden Markov models, multilayer-perceptron, and support vector machine [43][44][45]. One of the main problems in emotion recognition from speech is to find suitable features to represent the phenomenon.…”

Section: Voice Speech Emotion Recognitionmentioning

confidence: 99%

Improving Quality of Life: Home Care for Chronically Ill and Elderly People

Celani¹,

Ponce²,

Quintero³

et al. 2018

Caregiving and Home Care

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In this chapter, we propose a system especially created for elderly or chronically ill people that are with special needs and poor familiarity with technology. The system combines home monitoring of physiological and emotional states through a set of wearable sensors, user-controlled (automated) home devices, and a central control for integration of the data, in order to provide a safe and friendly environment according to the limited capabilities of the users. The main objective is to create the easy, low-cost automation of a room or house to provide a friendly environment that enhances the psychological condition of immobilized users. In addition, the complete interaction of the components provides an overview of the physical and emotional state of the user, building a behavior pattern that can be supervised by the care giving staff. This approach allows the integration of physiological signals with the patient's environmental and social context to obtain a complete framework of the emotional states.

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Speech-based recognition of self-reported and observed emotion in a dimensional space

Cited by 49 publications

References 53 publications

A satisfaction-based model for affect recognition from conversational features in spoken dialog systems

A satisfaction-based model for affect recognition from conversational features in spoken dialog systems

Mutual emotional understanding in a face-to-face communication environment: How speakers understand and react to listeners' emotion in a game task dialog

Improving Quality of Life: Home Care for Chronically Ill and Elderly People

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