Journal ArticleDOI
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
Semiye Demircan,Humar Kahramanli +1 more
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The results showed that using FCM for preprocessing aim increased the success rate of emotion recognition from speech signals and the maximum success rate was obtained as 92.86% using the SVM classifier.Abstract:
In the present study, emotion recognition from speech signals was performed by using the fuzzy C-means algorithm. Spectral features obtained from speech signals were used as features. The spectral features used were Mel frequency cepstral coefficients and linear prediction coefficients. Certain statistical features were extracted from the spectral features obtained in the study. After the selection of the extracted features, cluster centers were identified by using type-1 fuzzy C-means (FCM) algorithm and used as input to the classifier. Supervised classifiers such as ANN, NB, kNN, and SVM were used for classification. In the study, all seven emotions of the EmoDB database were used. Of the features obtained, FCM clustering was applied to Mel coefficients and obtained clusters centers were used as input for classification. The results showed that using FCM for preprocessing aim increased the success rate. The comparison of the classification methods showed that the maximum success rate was obtained as 92.86% using the SVM classifier.read more
Citations
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Journal ArticleDOI
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
TL;DR: The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion, especially the 2D CNN LSTM network outperforms the traditional approaches, Deep Belief Network (DBN) and CNN on the selected databases.
Journal ArticleDOI
Speech emotion recognition with deep convolutional neural networks
TL;DR: A new architecture is introduced, which extracts mel-frequency cepstral coefficients, chromagram, mel-scale spectrogram, Tonnetz representation, and spectral contrast features from sound files and uses them as inputs for the one-dimensional Convolutional Neural Network for the identification of emotions using samples from the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin, and EMO-DB datasets.
Journal ArticleDOI
CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network
Mustaqeem,Soonil Kwon +1 more
TL;DR: This paper addressed the limitations of the existing SER systems and proposed a unique artificial intelligence (AI) based system structure for the SER that utilizes the hierarchical blocks of the convolutional long short-term memory (ConvLSTM) with sequence learning.
Journal ArticleDOI
Emotion classification from speech signal based on empirical mode decomposition and non-linear features
TL;DR: An attempt to recognize seven emotional states from speech signals, known as sad, angry, disgust, happy, surprise, pleasant, and neutral sentiment, is investigated, which employs a non-linear signal quantifying method based on randomness measure,known as the entropy feature, for the detection of emotions.
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A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.