A Framework For Enhancing Speaker Age And Gender Classification By Using A New Feature Set And Deep Neural Network Architectures

Speaker age and gender classification is one of the most challenging problems in speech processing. Recently with developing technologies, identifying a speaker age and gender has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human-machine interaction, and adapting music for awaiting people queue. Although many studies have been carried out focusing on feature extraction and classifier design for improvement, classification accuracies are still not satisfactory. The key issue in identifying speaker’s age and gender is to generate robust features and to design an in-depth classifier. Age and gender information is concealed in speaker’s speech, which is liable for many factors such as, background noise, speech contents, and phonetic divergences. In this work, different methods are proposed to enhance the speaker age and gender classification based on the deep neural networks (DNNs) as a feature extractor and classifier. First, a model for generating new features from a DNN is proposed. The proposed method uses the Hidden Markov Model toolkit (HTK) tool to find tied-state triphones for all utterances, which are used as labels for the output layer in the DNN. The DNN with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers, then it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). Second, the shared class labels method is introduced among misclassified classes to regularize the weights in DNN. Third, DNN-based speakers models using the SDC feature set is proposed. The speakers-aware model can capture the characteristics of the speaker age and gender more effectively than a model that represents a group of speakers. In addition, AGender-Tune system is proposed to classify the speaker age and gender by jointly fine-tuning two DNN models; the first model is pre-trained to classify the speaker age, and second model is pre-trained to classify the speaker gender. Moreover, the new T-MFCCs feature set is used as the input of a fusion model of two systems. The first system is the DNN-based class model and the second system is the DNN-based speaker model. Utilizing the T-MFCCs as input and fusing the final score with the score of a DNN-based class model enhanced the classification accuracies. Finally, the DNN-based speaker models are embedded into an AGender-Tune system to exploit the advantages of each method for a better speaker age and gender classification. The experimental results on a public challenging database showed the effectiveness of the proposed methods for enhancing the speaker age and gender classification and achieved the state of the art on this database

A New Cost Function Combining Deep Neural Networks (DNNs) and l2,1-Norm with Extraction of Robust Facial and Superpixels Features in Age Estimation

Automatic age estimation from unconstrained facial images is a challenging task and it recently has gained much attention due to its wide range of applications. In this paper, we propose a new model based on convolutional neural networks (CNNs) and l2,1-norm to select age-related features for the age estimation task. A new cost function is proposed. To learn and train the new model, we provide the analysis and the proof for the convergence of the new cost function to solve minimization problem of deep neural networks (DNNs) and the l2,1-norm. High-level features are extracted from the facial images by using transfer learning, since there are currently not enough large age databases that can be used to train a deep learning network. Then, the extracted features are fed to the proposed model to select the most efficient age-related features. In addition, a new system that is based on DNN to jointly fine-tune two different DNNs with two different feature sets is developed. Experimental results show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database.http://dx.doi.org/10.3390/app810194

Machine Learning Based Feature Reduction for Network Intrusion Detection

The security of networked systems has become a critical universal issue. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset's feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6%. Furthermore, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset

Mental Suffering in Protracted Political Conflict: Feeling Broken or Destroyed.

PURPOSE:This mixed-methods exploratory study identified and then developed and validated a quantitative measure of a new construct of mental suffering in the occupied Palestinian territory: feeling broken or destroyed. METHODS:Group interviews were conducted in 2011 with 68 Palestinians, most aged 30-40, in the West Bank, East Jerusalem, and the Gaza Strip to discern local definitions of functioning. Interview participants articulated of a type of suffering not captured in existing mental health instruments used in regions of political conflict. In contrast to the specific difficulties measured by depression and PTSD (sleep, appetite, energy, flashbacks, avoidance, etc.), participants elaborated a more existential form of mental suffering: feeling that one's spirit, morale and/or future was broken or destroyed, and emotional and psychological exhaustion. Participants articulated these feelings when describing the rigors of the political and economic contexts in which they live. We wrote survey items to capture these sentiments and administered these items-along with standard survey measures of mental health-to a representative sample of 1,778 32-43 year olds in the occupied Palestinian territory. The same survey questions also were administered to a representative subsample (n = 508) six months earlier, providing repeated measures of the construct. RESULTS:Across samples and time, the feeling broken or destroyed scale: 1) comprised a separate factor in exploratory factor analyses, 2) had high inter-item consistency, 3) was reported by both genders and in all regions, 4) showed discriminate validity via moderate correlations with measures of feelings of depression and trauma-related stress, and 5) was more commonly experienced than either feelings of depression or trauma-related stress. CONCLUSIONS:Feeling broken or destroyed can be reliably measured and distinguished from conventional measures of mental health. Such locally grounded and contextualized measures should be identified and included in assessments of the full impact of protracted political conflict on functioning

Inter-item consistency (Cronbach’s alpha) for psychological functioning scales, by gender and region, within three datasets.

<p>Inter-item consistency (Cronbach’s alpha) for psychological functioning scales, by gender and region, within three datasets.</p

Frequency distributions (presented as percentages) for mental suffering variables, main sample.

<p>Frequency distributions (presented as percentages) for mental suffering variables, main sample.</p

Bivariate correlations among mental suffering scales, by gender and region within three datasets.

<p>Bivariate correlations among mental suffering scales, by gender and region within three datasets.</p

Range, means, and standard deviations for mental suffering measures, by region and gender, main sample.

<p>Range, means, and standard deviations for mental suffering measures, by region and gender, main sample.</p