Emotion recognition from speech signal using fuzzy clustering

Rovetta, Stefano; Mnasri, Zied; Masulli, Francesco; Cabri, Alberto

doi:10.2991/eusflat-19.2019.19

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The accuracy was 56% for anger, 78% for happiness, 100% for sadness, and 78% for neutral after using the SAVEE dataset. S. Rovetta [20] selected final features using Analysis of variance (ANOVA) or mutual information (MI) test. They also classified seven emotions (anger, neutral, disgust, sadness, boredom, fear, and joy) by applying the EMO-DB dataset in the fuzzy clustering method.…”

Section: Related Workmentioning

confidence: 99%

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Zisad

Hossain

Andersson

2020

Lecture Notes in Computer Science

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Detecting emotions from the speech is one of the emergent research fields in the area of human information processing. Expressing emotion is a very di cult task for a person with neurological disorder. Hence, a Speech Emotion Recognition (SER) system may solve this by ensuring a barrier-less communication. Various research has been carried out in the area of SER. Therefore, the main objective of this research is to develop a system that can recognize emotion from the speech of a neurologically disordered person. Since convolutional neural network (CNN) is an e↵ective method, it has been considered to develop the system. The system uses tonal properties like MFCCs. RAVDESS audio speech and song databases for training and testing. In addition, a custom local dataset developed to support further training and testing. The performance of the proposed system compared with the traditional machine learning models as well as with the pre-trained CNN models including VGG16 and VGG19. The results demonstrate that the CNN model proposed in this research performed better than the mentioned machine learning techniques. This system enables one tohhhhhh classify eight emotions of neurologically disordered person including calm, angry, fearful, disgust, happy, surprise, neutral and sad.

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Section: Related Workmentioning

confidence: 99%

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Zisad

Hossain

Andersson

2020

Lecture Notes in Computer Science

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“…Up to our knowledge, this is the first work totally relying on unsupervised learning, both for feature extraction and speech clustering. This work is an extension of results presented at the 11th Conference of the European Society for Fuzzy Logic and Technology [7].…”

Section: Introductionmentioning

confidence: 79%

Emotion Recognition from Speech: An Unsupervised Learning Approach

Rovetta¹,

Mnasri²,

Masulli³

et al. 2020

IJCIS

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Speech processing is quickly shifting toward affective computing, that requires handling emotions and modeling expressive speech synthesis and recognition. The latter task has been so far achieved by supervised classifiers. This implies a prior labeling and data preprocessing, with a cost that increases with the size of the database, in addition to the risk of committing errors. A typical emotion recognition corpus therefore has a relatively limited number of instances. To avoid the cost of labeling, and at the same time to reduce the risk of overfitting due to lack of data, unsupervised learning seems a suitable alternative to recognize emotions from speech. The recent advances in clustering techniques make it possible to reach good performances, comparable to that obtained by classifiers, with much less preprocessing load and even with generalization guarantees. This paper presents a novel approach for emotion recognition from speech signal, based on some variants of fuzzy clustering, such as probabilistic, possibilistic and graded-possibilistic fuzzy c-means. Experiments indicate that this approach (a) is effective in recognition, with in-corpus performances comparable to other proposals in the literature but with the added value of complexity control and (b) allows an innovative way to analyze emotions conveyed by speech using possibilistic membership degrees.

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“…Though the aforementioned features have reached outstanding performance in emotion recognition using supervised learning, they haven't been quite efficient while using clustering techniques [13]. Besides, such an important quantity of features induces a high dimensionality of the input space.…”

Section: Related Workmentioning

confidence: 99%

Emotional Content Comparison in Speech Signal Using Feature Embedding

Rovetta

Mnasri

Masulli

2020

Smart Innovation, Systems and Technologies

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Expressive speech processing has been improved in the recent years. However, it is still hard to detect emotion change in the same speech signal or to compare emotional content of a pair of speech signals, especially using unlabeled data. Therefore, feature embedding has been used in this work to enhance emotional content comparison for pairs of speech signals, cast as a classification task. Actually, feature embedding was proved to reduce the dimensionality and the intra-feature variance in the input space. Besides, deep autoencoders have recently been used as a feature embedding tool in several applications, such as image, gene and chemical data classification. In this work, a deep autoencoder is used for feature embedding before performing classification by vector quantization of the emotional content of pairs of speech signals. Autoencoding was performed following two schemes, for all features and for each group of features. The results show that the autoencoder succeeds (a) to reveal a more compact and a clearly separated structure of the mapped features, and (b) to improve the classification rates for the similarity/dissimilarity of all emotional content aspects that were compared, i.e neutrality, arousal and valence; in order to calculate the emotion identity metric.

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Emotion recognition from speech signal using fuzzy clustering

Cited by 3 publications

References 15 publications

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Emotion Recognition from Speech: An Unsupervised Learning Approach

Emotional Content Comparison in Speech Signal Using Feature Embedding

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