Fear-type emotion recognition for future audio-based surveillance systems

Clavel, Chloé; Vasilescu, Ioana; Devillers, Laurence; Richard, Gaël; Ehrette, Thibaut

doi:10.1016/j.specom.2008.03.012

Cited by 163 publications

(86 citation statements)

References 36 publications

(35 reference statements)

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“…Many previous efforts have addressed emotion recognition by employing pattern recognition techniques using segmental and/or supra-segmental information obtained from speech [6,[16][17][18][19][20][21][22][23][24][25][26]. Acoustic parameters of speech signal have been used extensively to separate emotional coloring present in the speech.…”

Section: Introductionmentioning

confidence: 99%

Recognizing emotion from Turkish speech using acoustic features

Oflazoglu

Yıldırım

2013

J AUDIO SPEECH MUSIC PROC.

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Affective computing, especially from speech, is one of the key steps toward building more natural and effective human-machine interaction. In recent years, several emotional speech corpora in different languages have been collected; however, Turkish is not among the languages that have been investigated in the context of emotion recognition. For this purpose, a new Turkish emotional speech database, which includes 5,100 utterances extracted from 55 Turkish movies, was constructed. Each utterance in the database is labeled with emotion categories (happy, surprised, sad, angry, fearful, neutral, and others) and three-dimensional emotional space (valence, activation, and dominance). We performed classification of four basic emotion classes (neutral, sad, happy, and angry) and estimation of emotion primitives using acoustic features. The importance of acoustic features in estimating the emotion primitive values and in classifying emotions into categories was also investigated. An unweighted average recall of 45.5% was obtained for the classification. For emotion dimension estimation, we obtained promising results for activation and dominance dimensions. For valence, however, the correlation between the averaged ratings of the evaluators and the estimates was low. The cross-corpus training and testing also showed good results for activation and dominance dimensions.

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Section: Introductionmentioning

confidence: 99%

Recognizing emotion from Turkish speech using acoustic features

Oflazoglu

Yıldırım

2013

J AUDIO SPEECH MUSIC PROC.

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“…Remote care assistance can benefit from the estimation of the affective state (e. g., stress or fear) in the voice of elder people [4]. Moreover, applications such as speech based advertising [5], remote teaching (e-learning) [6], job interview [7], and surveillance systems [8] may be incredibly enriched by customeraffect oriented services and monitoring, among many others.…”

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confidence: 99%

Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models

Mencattini

Martinelli

Ringeval

et al. 2017

IEEE Trans. Affective Comput.

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Abstract-Automatic emotion recognition from speech has been recently focused on the prediction of time-continuous dimensions (e.g., arousal and valence) of spontaneous and realistic expressions of emotion, as found in real-life interactions. However, the automatic prediction of such emotions poses several challenges, such as the subjectivity found in the definition of a gold standard from a pool of raters and the issue of data scarcity in training models. In this work, we introduce a novel emotion recognition system, based on ensemble of single-speaker-regression-models (SSRMs). The estimation of emotion is provided by combining a subset of the initial pool of SSRMs selecting those that are most concordance among them. The proposed approach allows the addition or removal of speakers from the ensemble without the necessity to re-build the entire machine learning system. The simplicity of this aggregation strategy, coupled with the flexibility assured by the modular architecture, and the promising results obtained on the RECOLA database highlight the potential implications of the proposed method in a real-life scenario and in particular in WEB-based applications.

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“…Because of this, the definition of emotions and its effect on speech signals might improve the speech performance and speaker recognition systems. Fear type emotion definition can be used in the voicebased control system to control a critical situation [5].…”

Section: Related Workmentioning

confidence: 99%

Wearable Vibration Based Computer Interaction and Communication System for Deaf

Yağanoğlu

Köse

2017

Applied Sciences

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Abstract:In individuals with impaired hearing, determining the direction of sound is a significant problem. The direction of sound was determined in this study, which allowed hearing impaired individuals to perceive where sounds originated. This study also determined whether something was being spoken loudly near the hearing impaired individual. In this manner, it was intended that they should be able to recognize panic conditions more quickly. The developed wearable system has four microphone inlets, two vibration motor outlets, and four Light Emitting Diode (LED) outlets. The vibration of motors placed on the right and left fingertips permits the indication of the direction of sound through specific vibration frequencies. This study applies the ReliefF feature selection method to evaluate every feature in comparison to other features and determine which features are more effective in the classification phase. This study primarily selects the best feature extraction and classification methods. Then, the prototype device has been tested using these selected methods on themselves. ReliefF feature selection methods are used in the studies; the success of K nearest neighborhood (Knn) classification had a 93% success rate and classification with Support Vector Machine (SVM) had a 94% success rate. At close range, SVM and two of the best feature methods were used and returned a 98% success rate. When testing our wearable devices on users in real time, we used a classification technique to detect the direction and our wearable devices responded in 0.68 s; this saves power in comparison to traditional direction detection methods. Meanwhile, if there was an echo in an indoor environment, the success rate increased; the echo canceller was disabled in environments without an echo to save power. We also compared our system with the localization algorithm based on the microphone array; the wearable device that we developed had a high success rate and it produced faster results at lower cost than other methods. This study provides a new idea for the benefit of deaf individuals that is preferable to a computer environment.

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Fear-type emotion recognition for future audio-based surveillance systems

Cited by 163 publications

References 36 publications

Recognizing emotion from Turkish speech using acoustic features

Recognizing emotion from Turkish speech using acoustic features

Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models

Wearable Vibration Based Computer Interaction and Communication System for Deaf

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