Emotion Verification Research Articles

Enhancing Criminal Detection: A Multi-Step Approach for Live Location Tracking and Emotion Verification Using Facial Recognition Technology

This paper offers a thorough analysis of the state of deceit detection in criminal justice and law enforcement settings as of right now. The study, which synthesizes findings from multiple investigations, emphasizes the progress made as well as the ongoing difficulties in accurately distinguishing deception from truth. The limitations of conventional techniques like behavior analysis interviews and polygraph exams, the potential of alternative strategies like voice tone analysis and facial expression analysis, and the moral ramifications of using emotional AI systems for deception detection are some of the main subjects covered. The review highlights the necessity for ongoing interdisciplinary research efforts and ethical concerns through critical analysis and discussion, in order to progress the field of deception detection while maintaining justice and respect to human rights values.

Vector learning representation for generalized speech emotion recognition

Speech emotion recognition (SER) plays an important role in global business today to improve service efficiency. In the literature of SER, many techniques have been using deep learning to extract and learn features. Recently, we have proposed end-to-end learning for a deep residual local feature learning block (DeepResLFLB). The advantages of end-to-end learning are low engineering effort and less hyperparameter tuning. Nevertheless, this learning method is easily to fall into an overfitting problem. Therefore, this paper described the concept of the “verify-to-classify” framework to apply for learning vectors, extracted from feature spaces of emotional information. This framework consists of two important portions: speech emotion learning and recognition. In speech emotion learning, consisting of two steps: speech emotion verification enrolled training and prediction, the residual learning (ResNet) with squeeze-excitation (SE) block was used as a core component of both steps to extract emotional state vectors and build an emotion model by the speech emotion verification enrolled training. Then the in-domain pre-trained weights of the emotion trained model are transferred to the prediction step. As a result of the speech emotion learning, the accepted model—validated by EER—is transferred to the speech emotion recognition in terms of out-domain pre-trained weights, which are ready for classification using a classical ML method. In this manner, a suitable loss function is important to work with emotional vectors. Here, two loss functions were proposed: angular prototypical and softmax with angular prototypical losses. Based on two publicly available datasets: Emo-DB and RAVDESS, both with high- and low-quality environments. The experimental results show that our proposed method can significantly improve generalized performance and explainable emotion results, when evaluated by standard metrics: EER, accuracy, precision, recall, and F1-score.

Emirati-Accented Emotion Verification based on HMM3s, HMM2s, and HMM1s

he proposed research is dedicated to verifying the claimed emotion of speaker-independent and text-independent formed on three dissimilar classifiers. The HMM3 short for Third-Order Hidden Markov Model, HMM2 short for Second-Order Hidden Markov Model, and HMM1 short for First-Order Hidden Markov Model are the three classifiers utilized in this study. Our work has been evaluated on our collected Emirati-accented speech corpus which entails 50 speakers of Emirati origin (25 female and 25 male) uttering sentences in six emotions by means of the extracted features by Mel-Frequency Cepstral Coefficients (MFCCs). Our outcomes prove that HMM3 is superior to each of HMM1 and HMM2 to authenticate the claimed emotion. The achieved results formed on HMM3 are very similar to the outcomes attained in the subjective valuation by Arab listeners.

Speech Emotion Verification Using Emotion Variance Modeling and Discriminant Scale-Frequency Maps

This paper develops an approach to speech-based emotion verification based on emotion variance modeling and discriminant scale-frequency maps. The proposed system consists of two parts-feature extraction and emotion verification. In the first part, for each sound frame, important atoms from the Gabor dictionary are selected by using the matching pursuit algorithm. The scale, frequency, and magnitude of the atoms are extracted to construct a nonuniform scale-frequency map, which supports auditory discriminability by the analysis of critical bands. Next, sparse representation is used to transform scale-frequency maps into sparse coefficients to enhance the robustness against emotion variance and achieve error-tolerance improvement. In the second part, emotion verification, two scores are calculated. A novel sparse representation verification approach based on Gaussian-modeled residual errors is proposed to generate the first score from the sparse coefficients. Such a classifier can minimize emotion variance and improve recognition accuracy. The second score is calculated by using the emotional agreement index (EAI) from the same coefficients. These two scores are combined to obtain the final detection result. Experiments on an emotional database of spoken speech were conducted and indicate that the proposed approach can achieve an average equal error rate (EER) of as low as 6.61%. A comparison among different approaches reveals that the proposed method is superior to the others and confirms its feasibility.

Music emotion detection using hierarchical sparse kernel machines.

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

Features extraction for speech emotion

In this paper the speech emotion verification using two most popular methods in speech processing and analysis based on the Mel-Frequency Cepstral Coefficient (MFCC) and the Gaussian Mixture Model (GMM) were proposed and analyzed. In both cases, features for the speech emotion were extracted using the Short Time Fourier Transform (STFT) and Short Time Histogram (STH) for MFCC and GMM respectively. The performance of the speech emotion verification is measured based on three neural network (NN) and fuzzy neural network (FNN) architectures; namely: Multi Layer Perceptron (MLP), Adaptive Neuro Fuzzy Inference System (ANFIS) and Generic Self-organizing Fuzzy Neural Network (GenSoFNN). Results obtained from the experiments using real audio clips from movies and television sitcoms show the potential of using the proposed features extraction methods for real time application due to its reasonable accuracy and fast training time. This may lead us to the practical usage if the emotion verifier can be embedded in real time applications especially for personal digital assistance (PDA) or smart-phones.

Features extraction for speech emotion

In this paper the speech emotion verification using two most popular methods in speech processing and analysis based on the Mel-Frequency Cepstral Coefficient (MFCC) and the Gaussian Mixture Model ...