Speech emotion classification using fractal dimension-based features

Tamulevičius, Gintautas; Karbauskaitė, Rasa; Dzemyda, Gintautas

doi:10.15388/na.2019.5.1

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…Other research has examined the deep learning method as a tool for neural data analysis performing speech classification and cross-frequency willing in the human sensorimotor cortex results, aiming to predict syllables resulting from high gamma cortical surface electrical potential data set recorded from the human sensorimotor cortex (17). Further research has been conducted by Tamulevičius et al (18) to classify speech emotions using fractal dimension-based features. The research has focused on effective feature sets, complex classification schemes, and multi-modal data acquisition.…”

Section: Related Studiesmentioning

confidence: 99%

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

et al. 2021

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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

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Section: Related Studiesmentioning

confidence: 99%

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

et al. 2021

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“…Fractal dimension characterizes and differentiates the irregularity, self-similarity, and nonlinearity between different speech emotions. In this study, we selected fractal dimension-based features, whose effectiveness in classifying speech emotion was justified in a previous study by Katz, Castiglioni, and Higuchi on fractal dimensions [6].…”

Section: Fractal Dimension-based Featuresmentioning

confidence: 99%

“…In the next step, the extracted features are classified to identify the emotional class that the analyzed speech utterance belongs to. Most current studies report speech emotion classification rates of 70%-90% [6].…”

Section: Introductionmentioning

confidence: 99%

A Study of Cross-Linguistic Speech Emotion Recognition Based on 2D Feature Spaces

et al. 2020

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In this research, a study of cross-linguistic speech emotion recognition is performed. For this purpose, emotional data of different languages (English, Lithuanian, German, Spanish, Serbian, and Polish) are collected, resulting in a cross-linguistic speech emotion dataset with the size of more than 10.000 emotional utterances. Despite the bi-modal character of the databases gathered, our focus is on the acoustic representation only. The assumption is that the speech audio signal carries sufficient emotional information to detect and retrieve it. Several two-dimensional acoustic feature spaces, such as cochleagrams, spectrograms, mel-cepstrograms, and fractal dimension-based space, are employed as the representations of speech emotional features. A convolutional neural network (CNN) is used as a classifier. The results show the superiority of cochleagrams over other feature spaces utilized. In the CNN-based speaker-independent cross-linguistic speech emotion recognition (SER) experiment, the accuracy of over 90% is achieved, which is close to the monolingual case of SER.

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“…Fractional derivatives of Gaussian noise can be used as assumptions for the excitation in an autoregressive model of speech [30]. There are successful applications of fractal features to speech recognition, voiced-unvoiced speech separation [31], and speaker emotion classification problems [32]. For example, combining fractal-geometry-based features produces comparable results to Mel-frequency Cepstral Coefficients in speech classification problems [33].…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Calculus-Based Data Augmentation Methods for Environmental Sound Classification with Deep Learning

Yazgac

Kırcı

2022

Fractal Fract

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In this paper, we propose two fractional-order calculus-based data augmentation methods for audio signals. The first approach is based on fractional differentiation of the Mel scale. By using a randomly selected fractional derivation order, we are warping the Mel scale, therefore, we aim to augment Mel-scale-based time-frequency representations of audio data. The second approach is based on previous fractional-order image edge enhancement methods. Since multiple deep learning approaches treat Mel spectrogram representations like images, a fractional-order differential-based mask is employed. The mask parameters are produced with respect to randomly selected fractional-order derivative parameters. The proposed data augmentation methods are applied to the UrbanSound8k environmental sound dataset. For the classification of the dataset and testing the methods, an arbitrary convolutional neural network is implemented. Our results show that fractional-order calculus-based methods can be employed as data augmentation methods. Increasing the dataset size to six times the original size, the classification accuracy result increased by around 8.5%. Additional tests on more complex networks also produced better accuracy results compared to a non-augmented dataset. To our knowledge, this paper is the first example of employing fractional-order calculus as an audio data augmentation tool.

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Speech emotion classification using fractal dimension-based features

Cited by 9 publications

References 45 publications

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

A Study of Cross-Linguistic Speech Emotion Recognition Based on 2D Feature Spaces

Fractional-Order Calculus-Based Data Augmentation Methods for Environmental Sound Classification with Deep Learning

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