EmoVoice — A Framework for Online Recognition of Emotions from Voice

Vogt, Thurid; André, Elisabeth; Bee, Nikolaus

doi:10.1007/978-3-540-69369-7_21

Cited by 116 publications

(79 citation statements)

References 13 publications

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“…However we have lower values for an extended set of the emotions. This is in accordance with [28,34] in which we expect low accuracy for completely normal low-intensity emotions in online recognition and decreasing the classification accuracy with the number of emotional categories. This issue requires further investigation and improvement.…”

Section: Resultssupporting

confidence: 92%

“…The NB classifier is very fast and appropriate for real-time emotion recognition. Our voice emotion recognition software supports speaker independent recognition approach, which is a general recognition system and therefore its accuracy is lower than the speaker dependent recognition approach that has been reported in [28].…”

Section: Voice Emotion Classificationmentioning

confidence: 93%

“…Such devices not only offer opportunities for more natural interactions with the online game-based learning applications, but also offer ways of gathering affective user data during learning. There is a growing body of studies on online and automatic voice emotion recognition systems [28,29,30]. In one well-known study [28] the researchers have presented a framework for classifier creation for both offline and real-time voice emotion recognition with a specific interface for German speakers.…”

Section: Introductionmentioning

confidence: 99%

“…There is a growing body of studies on online and automatic voice emotion recognition systems [28,29,30]. In one well-known study [28] the researchers have presented a framework for classifier creation for both offline and real-time voice emotion recognition with a specific interface for German speakers. They allowed creating a dataset for joy, satisfaction, anger, and frustration emotions in the context of a training procedure for non-expert users.…”

Section: Introductionmentioning

confidence: 99%

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FILTWAM and Voice Emotion Recognition

Bahreini

Nadolski

Westera

2014

Lecture Notes in Computer Science

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Abstract. This paper introduces the voice emotion recognition part of our framework for improving learning through webcams and microphones (FILTWAM). This framework enables multimodal emotion recognition of learners during game-based learning. The main goal of this study is to validate the use of microphone data for a real-time and adequate interpretation of vocal expressions into emotional states were the software is calibrated with end users. FILTWAM already incorporates a valid face emotion recognition module and is extended with a voice emotion recognition module. This extension aims to provide relevant and timely feedback based upon learner's vocal intonations. The feedback is expected to enhance learner's awareness of his or her own behavior. Six test persons received the same computer-based tasks in which they were requested to mimic specific vocal expressions. Each test person mimicked 82 emotions, which led to a dataset of 492 emotions. All sessions were recorded on video. An overall accuracy of our software based on the requested emotions and the recognized emotions is a pretty good 74.6% for the emotions happy and neutral emotions; but will be improved for the lower values of an extended set of emotions. In contrast with existing software our solution allows to continuously and unobtrusively monitor learners' intonations and convert these intonations into emotional states. This paves the way for enhancing the quality and efficacy of game-based learning by including the learner's emotional states, and links these to pedagogical scaffolding.

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Section: Resultssupporting

confidence: 92%

Section: Voice Emotion Classificationmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FILTWAM and Voice Emotion Recognition

Bahreini

Nadolski

Westera

2014

Lecture Notes in Computer Science

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“…At the second level, activation is applied. The reason for this choice is that when researchers limit themselves to a 2 dimensions emotional space instead of a 3 dimensions one, then this space is valence-activation [8] [14] [62]. Obviously, Fig.…”

Section: Psychologically-inspired Binary Cascade Classification Schemamentioning

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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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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Human Affect Recognition: Audio‐Based Methods

Schuller

Weninger

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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Automatic human affect recognition aims to automatically predict affect‐related information from humans observed for a certain time span. Such computer assessment of human emotion is described for audio‐based methods. This includes first acoustic analysis with suited features and segmentation of the speech signal. Then follows linguistic analysis including speech‐to‐text processing and representation of the text in a feature space, as well as joint acoustic–linguistic processing. Subsequently, data collection is described including emotion representation and elicitation. Further, automatic recognition by suited machine learning algorithms is touched upon. Then, solutions to cope with data scarcity, such as active, semi‐supervised, and transfer learning or even zero‐resource recognition, are explained. Following this, evaluation is discussed and typical benchmark results are given alongside standards and software. Next, application examples are given, before concluding by current research challenges.

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EmoVoice — A Framework for Online Recognition of Emotions from Voice

Cited by 116 publications

References 13 publications

FILTWAM and Voice Emotion Recognition

FILTWAM and Voice Emotion Recognition

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

Human Affect Recognition: Audio‐Based Methods

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