The Experts below are selected from a list of 9360 Experts worldwide ranked by ideXlab platform
Paavo Alku - One of the best experts on this subject based on the ideXlab platform.
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Excitation Features of Speech for Speaker-Specific Emotion Detection
IEEE Access, 2020Co-Authors: Sudarsana Reddy Kadiri, Paavo AlkuAbstract:In this article, we study Emotion Detection from speech in a speaker-specific scenario. By parameterizing the excitation component of voiced speech, the study explores deviations between Emotional speech (e.g., speech produced in anger, happiness, sadness, etc.) and neutral speech (i.e., non-Emotional) to develop an automatic Emotion Detection system. The excitation features used in this study are the instantaneous fundamental frequency, the strength of excitation and the energy of excitation. The Kullback-Leibler (KL) distance is computed to measure the similarity between feature distributions of Emotional and neutral speech. Based on the KL distance value between a test utterance and an utterance produced in a neutral state by the same speaker, a Detection decision is made by the system. In the training of the proposed system, only three neutral utterances produced by the speaker were used, unlike in most existing Emotion recognition and Detection systems that call for large amounts of training data (both Emotional and neutral) by several speakers. In addition, the proposed system is independent of language or lexical content. The system is evaluated using two databases of Emotional speech. The performance of the proposed Detection method is shown to be better than that of reference methods.
Ioana Vasilescu - One of the best experts on this subject based on the ideXlab platform.
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Emotion Detection in task oriented spoken dialogues
International Conference on Multimedia and Expo, 2003Co-Authors: Laurence Devillers, Lori Lamel, Ioana VasilescuAbstract:Detecting Emotions in the context of automated call center services can be helpful for following the evolution of the human-computer dialogues, enabling dynamic modification of the dialogue strategies and influencing the final outcome. The Emotion Detection work reported here is a part of larger study aiming to model user behavior in real interactions. We make use of a corpus of real agent-client spoken dialogues in which the manifestation of Emotion is quite complex, and it is common to have shaded Emotions since the interlocutors attempt to control the expression of their internal attitude. Our aims are to define appropriate Emotions for call center services, to annotate the dialogues and to validate the presence of Emotions via perceptual tests and to find robust cues for Emotion Detection. In contrast to research carried out with artificial data with simulated Emotions, for real-life corpora the set of appropriate Emotion labels must be determined. Two studies are reported: the first investigates automatic Emotion Detection using linguistic information, whereas the second concerns perceptual tests for identifying Emotions as well as the prosodic and textual cues which signal them. About 11% of the utterances are annotated with non-neutral Emotion labels. Preliminary experiments using lexical cues detect about 70% of these labels.
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ICME - Emotion Detection in task-oriented spoken dialogues
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003Co-Authors: Laurence Devillers, Lori Lamel, Ioana VasilescuAbstract:Detecting Emotions in the context of automated call center services can be helpful for following the evolution of the human-computer dialogues, enabling dynamic modification of the dialogue strategies and influencing the final outcome. The Emotion Detection work reported here is a part of larger study aiming to model user behavior in real interactions. We make use of a corpus of real agent-client spoken dialogues in which the manifestation of Emotion is quite complex, and it is common to have shaded Emotions since the interlocutors attempt to control the expression of their internal attitude. Our aims are to define appropriate Emotions for call center services, to annotate the dialogues and to validate the presence of Emotions via perceptual tests and to find robust cues for Emotion Detection. In contrast to research carried out with artificial data with simulated Emotions, for real-life corpora the set of appropriate Emotion labels must be determined. Two studies are reported: the first investigates automatic Emotion Detection using linguistic information, whereas the second concerns perceptual tests for identifying Emotions as well as the prosodic and textual cues which signal them. About 11% of the utterances are annotated with non-neutral Emotion labels. Preliminary experiments using lexical cues detect about 70% of these labels.
Mitsunori Ogihara - One of the best experts on this subject based on the ideXlab platform.
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Content-based music similarity search and Emotion Detection
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2004Co-Authors: Tao Li, Mitsunori OgiharaAbstract:The paper investigates the use of acoustic based features for music information retrieval. Two specific problems are studied: similarity search (searching for music sound files similar to a given music sound file) and Emotion Detection (Detection of Emotion in music sounds). The Daubechies wavelet coefficient histograms (Li, T. et al., SIGIR'03, p.282-9, 2003), which consist of moments of the coefficients calculated by applying the Db8 wavelet filter, are combined with the timbral features extracted using the MARSYAS system of G. Tzanctakis and P. Cook (see IEEE Trans. on Speech and Audio Process., vol.10, no.5, p.293-8, 2002) to generate compact music features. For the similarity search, the distance between two sound files is defined to be the Euclidean distance of their normalized representations. Based on the distance measure, the closest sound files to an input sound file are obtained. Experiments on jazz vocal and classical sound files achieve a very high level of accuracy. Emotion Detection is cast as a multiclass classification problem, decomposed as a multiple binary classification problem, and is resolved with the use of support vector machines trained on the extracted features. Our experiments on Emotion Detection achieved reasonably accurate performance and provided some insights on future work.
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ICASSP (5) - Content-based music similarity search and Emotion Detection
2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Mitsunori OgiharaAbstract:The paper investigates the use of acoustic based features for music information retrieval. Two specific problems are studied: similarity search (searching for music sound files similar to a given music sound file) and Emotion Detection (Detection of Emotion in music sounds). The Daubechies wavelet coefficient histograms (Li, T. et al., SIGIR'03, p.282-9, 2003), which consist of moments of the coefficients calculated by applying the Db8 wavelet filter, are combined with the timbral features extracted using the MARSYAS system of G. Tzanctakis and P. Cook (see IEEE Trans. on Speech and Audio Process., vol.10, no.5, p.293-8, 2002) to generate compact music features. For the similarity search, the distance between two sound files is defined to be the Euclidean distance of their normalized representations. Based on the distance measure, the closest sound files to an input sound file are obtained. Experiments on jazz vocal and classical sound files achieve a very high level of accuracy. Emotion Detection is cast as a multiclass classification problem, decomposed as a multiple binary classification problem, and is resolved with the use of support vector machines trained on the extracted features. Our experiments on Emotion Detection achieved reasonably accurate performance and provided some insights on future work.
Sudarsana Reddy Kadiri - One of the best experts on this subject based on the ideXlab platform.
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Excitation Features of Speech for Speaker-Specific Emotion Detection
IEEE Access, 2020Co-Authors: Sudarsana Reddy Kadiri, Paavo AlkuAbstract:In this article, we study Emotion Detection from speech in a speaker-specific scenario. By parameterizing the excitation component of voiced speech, the study explores deviations between Emotional speech (e.g., speech produced in anger, happiness, sadness, etc.) and neutral speech (i.e., non-Emotional) to develop an automatic Emotion Detection system. The excitation features used in this study are the instantaneous fundamental frequency, the strength of excitation and the energy of excitation. The Kullback-Leibler (KL) distance is computed to measure the similarity between feature distributions of Emotional and neutral speech. Based on the KL distance value between a test utterance and an utterance produced in a neutral state by the same speaker, a Detection decision is made by the system. In the training of the proposed system, only three neutral utterances produced by the speaker were used, unlike in most existing Emotion recognition and Detection systems that call for large amounts of training data (both Emotional and neutral) by several speakers. In addition, the proposed system is independent of language or lexical content. The system is evaluated using two databases of Emotional speech. The performance of the proposed Detection method is shown to be better than that of reference methods.
Laurence Devillers - One of the best experts on this subject based on the ideXlab platform.
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Emotion Detection in task oriented spoken dialogues
International Conference on Multimedia and Expo, 2003Co-Authors: Laurence Devillers, Lori Lamel, Ioana VasilescuAbstract:Detecting Emotions in the context of automated call center services can be helpful for following the evolution of the human-computer dialogues, enabling dynamic modification of the dialogue strategies and influencing the final outcome. The Emotion Detection work reported here is a part of larger study aiming to model user behavior in real interactions. We make use of a corpus of real agent-client spoken dialogues in which the manifestation of Emotion is quite complex, and it is common to have shaded Emotions since the interlocutors attempt to control the expression of their internal attitude. Our aims are to define appropriate Emotions for call center services, to annotate the dialogues and to validate the presence of Emotions via perceptual tests and to find robust cues for Emotion Detection. In contrast to research carried out with artificial data with simulated Emotions, for real-life corpora the set of appropriate Emotion labels must be determined. Two studies are reported: the first investigates automatic Emotion Detection using linguistic information, whereas the second concerns perceptual tests for identifying Emotions as well as the prosodic and textual cues which signal them. About 11% of the utterances are annotated with non-neutral Emotion labels. Preliminary experiments using lexical cues detect about 70% of these labels.
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ICME - Emotion Detection in task-oriented spoken dialogues
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003Co-Authors: Laurence Devillers, Lori Lamel, Ioana VasilescuAbstract:Detecting Emotions in the context of automated call center services can be helpful for following the evolution of the human-computer dialogues, enabling dynamic modification of the dialogue strategies and influencing the final outcome. The Emotion Detection work reported here is a part of larger study aiming to model user behavior in real interactions. We make use of a corpus of real agent-client spoken dialogues in which the manifestation of Emotion is quite complex, and it is common to have shaded Emotions since the interlocutors attempt to control the expression of their internal attitude. Our aims are to define appropriate Emotions for call center services, to annotate the dialogues and to validate the presence of Emotions via perceptual tests and to find robust cues for Emotion Detection. In contrast to research carried out with artificial data with simulated Emotions, for real-life corpora the set of appropriate Emotion labels must be determined. Two studies are reported: the first investigates automatic Emotion Detection using linguistic information, whereas the second concerns perceptual tests for identifying Emotions as well as the prosodic and textual cues which signal them. About 11% of the utterances are annotated with non-neutral Emotion labels. Preliminary experiments using lexical cues detect about 70% of these labels.