Age and gender classification from speech and face images by jointly fine-tuned deep neural networks

Qawaqneh, Zakariya; Mallouh, Arafat Abu; Barkana, Buket D.

doi:10.1016/j.eswa.2017.05.037

Cited by 57 publications

(30 citation statements)

References 9 publications

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“…e ordinary frequency is converted to Mel frequency, and the first 12 steps of MFCC are extracted. e most widely used classifiers for speech recognition are HMM [25,26], GMM [27,28], ANN [29,30], and SVM [31,32]. In this paper, to improve the separability of data, the SVM classifier is used to generate a nonlinear mapping of the original features to a high-dimensional space; the choice of kernel function is Radial Basis Function (RBF).…”

Section: Mfcc Feature Extractionmentioning

confidence: 99%

Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

Sun

Zhang

Song

et al. 2020

Mathematical Problems in Engineering

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Due to the shortcomings of linear feature parameters in speech signals, and the limitations of existing time- and frequency-domain attribute features in characterizing the integrity of the speech information, in this paper, we propose a nonlinear method for feature extraction based on the phase space reconstruction (PSR) theory. First, the speech signal was analyzed using a nonlinear dynamic model. Then, the model was used to reconstruct a one-dimensional time speech signal. Finally, nonlinear dynamic (NLD) features based on the reconstruction of the phase space were extracted as the new characteristic parameters. Then, the performance of NLD features was verified by comparing their recognition rates with those of other features (NLD features, prosodic features, and MFCC features). Finally, the Korean isolated words database, the Berlin emotional speech database, and the CASIA emotional speech database were chosen for validation. The effectiveness of the NLD features was tested using the Support Vector Machine classifier. The results show that NLD features not only have high recognition rate and excellent antinoise performance for speech recognition tasks but also can fully characterize the different emotions contained in speech signals.

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Section: Mfcc Feature Extractionmentioning

confidence: 99%

Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

Sun

Zhang

Song

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A resurgence of interest has been seen in last few years towards artificial neural networks, specifically deep learning has been used extensively after its spectacular success in the area of image classification, regression problems in time series data, and natural language processing [44][45][46]. This has been used in stock market analysis and prediction [47][48][49], breast cancer classification [50], classification of electrocardiogram signals [51], image classification [52], object recognition [53], medical image analysis [54], and time-series data analysis [55]. Deep learning, otherwise known as feature learning, has been well suited for deriving basic features from the input data and the obtained extracted features can be used to train a model in a better way.…”

Section: Related Workmentioning

confidence: 99%

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

2018

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The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models’ accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments.

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“…They argue that the proposed method achieves higher success than the existing methods. [19] developed a new cost function from the well-known softmax and sigmoid functions for concurrent and jointly fine-tuned deep neural networks to classify age and gender from speech and face images. They proposed a new deep neural network architecture.…”

Section: Introductionmentioning

confidence: 99%

Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks

Hacıbeyoğlu

Ibrahim

2018

ijisae

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The interest in automatic gender classification has increased rapidly, especially with the growth of online social networking platforms, social media applications, and commercial applications. Most of the images shared on these platforms are taken by mobile phone with different expressions, different angles and low resolution. In recent years, convolutional neural networks have become the most powerful method for image classification. Many researchers have shown that convolutional neural networks can achieve better performance by modifying different network layers of network architecture. Moreover, the selection of the appropriate activation function of neurons, optimizer and the loss function directly affects the performance of the convolutional neural networks. In this study, we propose a gender classification system from facial images taken by mobile phone using convolutional neural networks. The proposed convolutional neural networks have a simple network architecture with appropriate parameters can be used when rapid training is needed with the amount of limited training data. In the experimental study, the Adience benchmark dataset was used with 17492 different images with different gender and ages. The classification process was carried out by 10-fold cross validation. According the experimental results, the proposed convolutional neural networks predicted the gender of the images 98.87% correctly for training and 89.13% for testing.

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Age and gender classification from speech and face images by jointly fine-tuned deep neural networks

Cited by 57 publications

References 9 publications

Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks

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