Improving the performance of the speaker emotion recognition based on low dimension prosody features vector

Ashishkumar, Gudmalwar; Rao, Ch. V. Rama; Dutta, Anirban

doi:10.1007/s10772-018-09576-4

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…In [46], semi-NMF with k-means clustering initialization was used to transform feature sets, which were further combined with the original dataset to obtain a total of 72 features for SER obtaining 77.74% accuracy. In [47][48][49][50][51][52], different optimizing and feature selection techniques, namely enhanced kernel isometric mapping, the modified supervised locally linear embedding algorithm, sparse partial least squares regression, sequential floating forward selection, the scaled conjugate gradient, and principal component analysis, were used for improving the classification accuracy by reducing the feature set dimension. However, the classification accuracy obtained with the proposed SER system is higher than the other methods with 90% (approx.)…”

Section: Resultsmentioning

confidence: 99%

Speech emotion recognition using semi-NMF feature optimization

Bandela¹,

Kumar²

2019

Turk J Elec Eng & Comp Sci

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In recent times, much research is progressing forward in the field of speech emotion recognition (SER). Many SER systems have been developed by combining different speech features to improve their performances. As a result, the complexity of the classifier increases to train this huge feature set. Additionally, some of the features could be irrelevant in emotion detection and this leads to a decrease in the emotion recognition accuracy. To overcome this drawback, feature optimization can be performed on the feature sets to obtain the most desirable emotional feature set before classifying the features. In this paper, semi-nonnegative matrix factorization (semi-NMF) with singular value decomposition (SVD) initialization is used to optimize the speech features. The speech features considered in this work are mel-frequency cepstral coefficients, linear prediction cepstral coefficients, and Teager energy operator-autocorrelation (TEO-AutoCorr).This work uses k-nearest neighborhood and support vector machine (SVM) for the classification of emotions with a 5-fold cross-validation scheme. The datasets considered for the performance analysis are EMO-DB and IEMOCAP. The performance of the proposed SER system using semi-NMF is validated in terms of classification accuracy. The results emphasize that the accuracy of the proposed SER system is improved remarkably upon using the semi-NMF algorithm for optimizing the feature sets compared to the baseline SER system without optimization.

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Section: Resultsmentioning

confidence: 99%

Speech emotion recognition using semi-NMF feature optimization

Bandela¹,

Kumar²

2019

Turk J Elec Eng & Comp Sci

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“…The DAN is a type of deep learning, which is advanced machine learning. Deep learning is widely employed in artificial intelligence domains such as medicine [18], images processing [19], information and sound application [20], computer vision [21], and many more applications [15], [22]- [28]. In this paper, the DAN is suggested, implemented and evaluated.…”

Section: Deep Autoencoder Networkmentioning

confidence: 99%

Signal multiple encodings by using autoencoder deep learning

Anaz

Al-Ridha²,

Al-Nima³

2023

Bulletin EEI

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Encryption is a substantial phase in information security. It permits only approved persons to get private information. This study suggests a signal multi-encryptions system (SMES) technique for coding and decoding signals created by a deep autoencoder network (DAN). The DAN of four layers is employed for a coding package of signals multiple times before decoding or restructuring the original signals again. The suggested SMES offers a high level of security as it can produce and exploit multiple encryptions for signals. Many statistical calculations are applied to measure the reliability of the system. The outcomes are promising where noteworthy encryptions-decryptions are obtained.

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“…Facial landmark recognition algorithms are computer vision techniques designed to identify and locate specific facial landmarks or keypoints on a human face. These algorithms play a crucial role in various applications such as face analysis, facial expression recognition, face tracking, augmented reality, and drowsiness detection systems [23]. Finding the face in the image as well as identifying the points which create the face structure are the goals of Facial landmark.…”

Section: Facial Landmark Recognition Algorithmmentioning

confidence: 99%

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Majeed,

Hussein Ali,

A. Al-Hilali

et al. 2023

Bulletin EEI

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Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations.

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Improving the performance of the speaker emotion recognition based on low dimension prosody features vector

Cited by 11 publications

References 33 publications

Speech emotion recognition using semi-NMF feature optimization

Speech emotion recognition using semi-NMF feature optimization

Signal multiple encodings by using autoencoder deep learning

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

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