Automatic speaker recognition

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır

Temporal Pattern Classification using Kernel Methods for Speech

There are two paradigms for modelling the varying length temporal data namely, modelling the sequences of feature vectors as in the hidden Markov model-based approaches for speech recognition and modelling the sets of feature vectors as in the Gaussian mixture model (GMM)-based approaches for speech emotion recognition. In this paper, the methods using discrete hidden Markov models (DHMMs) in the kernel feature space and string kernel-based SVM classifier for classification of discretised representation of sequence of feature vectors obtained by clustering and vector quantisation in the kernel feature space are presented. The authors then present continuous density hidden Markov models (CDHMMs) in the explicit kernel feature space that use the continuous valued representation of features extracted from the temporal data. The methods for temporal pattern classification by mapping a varying length sequential pattern to a fixed-length sequential pattern and then using an SVM-based classifier for classification are also presented. The task of recognition of spoken letters in E-set, it is possible to build models that use a discretised representation and string kernel SVM based classification and obtain a classification performance better than that of models using the continuous valued representation is demonstrated. For modelling sets of vectors-based representation of temporal data, two approaches in a hybrid framework namely, the score vector-based approach and the segment modelling based approach are presented. In both approaches, a generative model-based method is used to obtain a fixed length pattern representation for a varying length temporal data and then a discriminative model is used for classification. These two approaches are studied for speech emotion recognition task. The segment modelling based approach gives a better performance than the score vector-based approach and the GMM-based classifiers for speech emotion recognition.Defence Science Journal, 2010, 60(4), pp.348-363, DOI:http://dx.doi.org/10.14429/dsj.60.49

Emotion Recognition from Speech using GMM and VQ

In this paper, there is a tendency to study the effectiveness of anchor models applied to the multiclass drawback of Emotion recognition from speech. Within the anchor models system, Associate in nursing emotion category is characterized by its line of similarity relative to different emotion categories. Generative models like Gaussian Mixture Models (GMMs) are typically used as front-end systems to get feature vectors wont to train complicated back-end systems like support vector machines (SVMs) or a multilayer perceptron (MLP) to enhance the classification performance. There is a tendency to show that within the context of extremely unbalanced knowledge categories, these back-end systems will improve the performance achieved by GMMs as long as Associate in nursing acceptable sampling or importance coefficient technique is applied. The experiments conducted on audio sample of speech; show that anchor models improve considerably the performance of GMMs by half dozen.2 % relative. There is a tendency to be employing a hybrid approach for recognizing emotion from speech that may be a combination of Vector quantization (VQ) and mathematician Mixture Models (GMM). A quick review of labor applied within the space of recognition victimization VQ-GMM hybrid approach is mentioned here. DOI: 10.17762/ijritcc2321-8169.15082

Deception Detection in Videos

We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5%. When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible. Our project page can be found at \url{https://doubaibai.github.io/DARE/}.Comment: AAAI 2018, project page: https://doubaibai.github.io/DARE

Speech data analysis for semantic indexing of video of simulated medical crises.

The Simulation for Pediatric Assessment, Resuscitation, and Communication (SPARC) group within the Department of Pediatrics at the University of Louisville, was established to enhance the care of children by using simulation based educational methodologies to improve patient safety and strengthen clinician-patient interactions. After each simulation session, the physician must manually review and annotate the recordings and then debrief the trainees. The physician responsible for the simulation has recorded 100s of videos, and is seeking solutions that can automate the process. This dissertation introduces our developed system for efficient segmentation and semantic indexing of videos of medical simulations using machine learning methods. It provides the physician with automated tools to review important sections of the simulation by identifying who spoke, when and what was his/her emotion. Only audio information is extracted and analyzed because the quality of the image recording is low and the visual environment is static for most parts. Our proposed system includes four main components: preprocessing, speaker segmentation, speaker identification, and emotion recognition. The preprocessing consists of first extracting the audio component from the video recording. Then, extracting various low-level audio features to detect and remove silence segments. We investigate and compare two different approaches for this task. The first one is threshold-based and the second one is classification-based. The second main component of the proposed system consists of detecting speaker changing points for the purpose of segmenting the audio stream. We propose two fusion methods for this task. The speaker identification and emotion recognition components of our system are designed to provide users the capability to browse the video and retrieve shots that identify ”who spoke, when, and the speaker’s emotion” for further analysis. For this component, we propose two feature representation methods that map audio segments of arbitary length to a feature vector with fixed dimensions. The first one is based on soft bag-of-word (BoW) feature representations. In particular, we define three types of BoW that are based on crisp, fuzzy, and possibilistic voting. The second feature representation is a generalization of the BoW and is based on Fisher Vector (FV). FV uses the Fisher Kernel principle and combines the benefits of generative and discriminative approaches. The proposed feature representations are used within two learning frameworks. The first one is supervised learning and assumes that a large collection of labeled training data is available. Within this framework, we use standard classifiers including K-nearest neighbor (K-NN), support vector machine (SVM), and Naive Bayes. The second framework is based on semi-supervised learning where only a limited amount of labeled training samples are available. We use an approach that is based on label propagation. Our proposed algorithms were evaluated using 15 medical simulation sessions. The results were analyzed and compared to those obtained using state-of-the-art algorithms. We show that our proposed speech segmentation fusion algorithms and feature mappings outperform existing methods. We also integrated all proposed algorithms and developed a GUI prototype system for subjective evaluation. This prototype processes medical simulation video and provides the user with a visual summary of the different speech segments. It also allows the user to browse videos and retrieve scenes that provide answers to semantic queries such as: who spoke and when; who interrupted who? and what was the emotion of the speaker? The GUI prototype can also provide summary statistics of each simulation video. Examples include: for how long did each person spoke? What is the longest uninterrupted speech segment? Is there an unusual large number of pauses within the speech segment of a given speaker

Multimodal Sensing and Data Processing for Speaker and Emotion Recognition using Deep Learning Models with Audio, Video and Biomedical Sensors

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection. The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker recognition or as a substitute in noisy or poorly-lit environments. Secondly, the effectiveness of deep learning is empirically confirmed through its higher robustness to all types of features in comparison to a number of commonly used baseline classifiers. Not only do deep models outperform the baseline methods, their power increases when they integrate multiple modalities, as different modalities contain information on different aspects of the data, especially between EMG and audio. Interestingly, our deep learning approach is word-independent. Plus, the EMG, audio, and visual parts of the samples from each speaker do not need to match. This increases the flexibility of our method in using multimodal data, particularly if one or more modalities are missing. With a dataset of 23 individuals speaking 22 words five times, we show that EMG can replace the audio/visual modalities, and when combined, significantly improve the accuracy of speaker recognition. The second part describes a study on automated emotion recognition using four different modalities – audio, video, electromyography (EMG), and electroencephalography (EEG). We collected a dataset by recording the 4 modalities as 12 human subjects expressed six different emotions or maintained a neutral expression. Three different aspects of emotion recognition were investigated: model selection, feature selection, and data selection. Both generative models (DBNs) and discriminative models (LSTMs) were applied to the four modalities, and from these analyses we conclude that LSTM is better for audio and video together with their corresponding sophisticated feature extractors (MFCC and CNN), whereas DBN is better for both EMG and EEG. By examining these signals at different stages (pre-speech, during-speech, and post-speech) of the current and following trials, we have found that the most effective stages for emotion recognition from EEG occur after the emotion has been expressed, suggesting that the neural signals conveying an emotion are long-lasting

Fusion for Audio-Visual Laughter Detection

Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA-PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio. We compared hidden Markov models (HMMs), Gaussian mixture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better performance than single-modality classification