Feature extraction algorithms to improve the speech emotion recognition rate

Koduru, Anusha; Valiveti, Hima Bindu; Budati, Anil Kumar

doi:10.1007/s10772-020-09672-4

Cited by 137 publications

(49 citation statements)

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“…LPCC are derived from the Fourier transform of the log magnitude spectrum of LPC. The input signal is analyzed by approximating the frequency bands [7] by removing their effects from the signal and approximates the intensity and frequency of the remaining signal. With the help of Discrete Wavelet Transform (DWT), time domain and frequency domain information of the signal can be fetched.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Each pattern used for the training of the classifier carries the correct emotion/gender class label. The most popular approaches for classifications include Bayesian learning, the Linear Discriminant Analysis (LDA), the Support Vector Machine (SVM) [7] which is used as an extension of LDA with a high-dimensional feature space, the multi-layer Neu-ral Network (NN), and the Hidden Markov model (HMM) which captures temporal state transitions. SVM is the most widely used classifier due to its efficiency in classifying high dimensional data where the number of features is greater than number of observations.…”

Section: E Classificationmentioning

confidence: 99%

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Voice Feature Extraction for Gender and Emotion Recognition

Nair

Pillai

Subramanian

et al. 2021

IJRITCC

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Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual differences along with a variety of emotions which convey their own unique perceptions. In order to explore this area, feature extraction requires pre- processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre- processing using Voice Activity Detector (VAD), feature extraction using Mel-Frequency Cepstral Coefficient (MFCC), feature reduction by Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in the healthcare sector, virtual assistants, security purposes and other fields related to the Human Machine Interaction domain.

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Section: Feature Extractionmentioning

confidence: 99%

Section: E Classificationmentioning

confidence: 99%

Voice Feature Extraction for Gender and Emotion Recognition

Nair

Pillai

Subramanian

et al. 2021

IJRITCC

View full text Add to dashboard Cite

show abstract

“…To obtain ground truth depth values and obtain high performance, we employed a CNN deep learning model and a supervised deep learning-based technique. Monocular depth estimation relies on image processing techniques [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and also helpful during communication [29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Monocular Depth Estimation using Transfer learning-An Overview

Swaraja¹,

Akshitha²,

B³

et al. 2021

E3S Web Conf.

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Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.

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“…In recent years, as computers have increasingly powerful computing power and huge data sets continue to emerge, machine learning algorithms (Domínguez-Jiménez et al, 2020;Zhang et al, 2020;Cai et al, 2021a) have developed vigorously. Compared with traditional methods, the machine learning algorithm integrates the two processes of feature extraction (Koduru et al, 2020) and classification (Oberländer and Klinger, 2018), reduces the operation process, and can automatically extract the internal features of the sample data, has powerful feature extraction capabilities, and is related to computer vision (CV) (Schmøkel and Bossetta, 2021;Cai et al, 2021b). The performance in various competitions is very good.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Song

2021

Front. Psychol.

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Facial expression emotion recognition is an intuitive reflection of a person’s mental state, which contains rich emotional information, and is one of the most important forms of interpersonal communication. It can be used in various fields, including psychology. As a celebrity in ancient China, Zeng Guofan’s wisdom involves facial emotion recognition techniques. His book Bing Jian summarizes eight methods on how to identify people, especially how to choose the right one, which means “look at the eyes and nose for evil and righteousness, the lips for truth and falsehood; the temperament for success and fame, the spirit for wealth and fortune; the fingers and claws for ideas, the hamstrings for setback; if you want to know his consecution, you can focus on what he has said.” It is said that a person’s personality, mind, goodness, and badness can be showed by his face. However, due to the complexity and variability of human facial expression emotion features, traditional facial expression emotion recognition technology has the disadvantages of insufficient feature extraction and susceptibility to external environmental influences. Therefore, this article proposes a novel feature fusion dual-channel expression recognition algorithm based on machine learning theory and philosophical thinking. Specifically, the feature extracted using convolutional neural network (CNN) ignores the problem of subtle changes in facial expressions. The first path of the proposed algorithm takes the Gabor feature of the ROI area as input. In order to make full use of the detailed features of the active facial expression emotion area, first segment the active facial expression emotion area from the original face image, and use the Gabor transform to extract the emotion features of the area. Focus on the detailed description of the local area. The second path proposes an efficient channel attention network based on depth separable convolution to improve linear bottleneck structure, reduce network complexity, and prevent overfitting by designing an efficient attention module that combines the depth of the feature map with spatial information. It focuses more on extracting important features, improves emotion recognition accuracy, and outperforms the competition on the FER2013 dataset.

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Feature extraction algorithms to improve the speech emotion recognition rate

Cited by 137 publications

References 10 publications

Voice Feature Extraction for Gender and Emotion Recognition

Voice Feature Extraction for Gender and Emotion Recognition

Monocular Depth Estimation using Transfer learning-An Overview

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

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