Speech signals carry important information about a speaker such as age, gender, language, accent and emotional/psychological state. Automatic recognition of speaker characteristics has a wide range of commercial, medical and forensic applications such as interactive voice response systems, service customization, natural human-machine interaction, recognizing the type of pathology of speakers, and directing the forensic investigation process. This research aims to develop accurate methods and tools to identify different physical characteristics of the speakers. Due to the lack of required databases, among all characteristics of speakers, our experiments cover gender recognition, age estimation, language recognition and accent/dialect identification. However, similar approaches and techniques can be applied to identify other characteristics such as emotional/psychological state. For speaker characterization, we first convert variable-duration speech signals into fixed-dimensional vectors suitable for classification/regression algorithms. This is performed by fitting a probability density function to acoustic ...

Current Human-Machine Interfaces (HMI) lack of “emotional intelligence”, i.e. they are not able to identify human emotional states and take this information into account to decide on the proper actions to execute. The goal of affective computing is to fill this lack by detecting emotional cues occurring during Human-Computer Interaction (HCI) and synthesizing emotional responses. In the last decades, most of the studies on emotion assessment have focused on the analysis of facial expressions and speech to determine the emotional state of a person. Physiological activity also includes emotional information that can be used for emotion assessment but has received less attention despite of its advantages (for instance it can be less easily faked than facial expressions). This thesis reports on the use of two types of physiological activities to assess emotions in the context of affective computing: the activity ...

Advances in compression techniques, decreasing cost of storage, and high-speed transmission have facilitated the way videos are created, stored and distributed. As a consequence, videos are now being used in many applications areas. The increase in the amount of video data deployed and used in today's applications reveals not only the importance as multimedia data type, but also led to the requirement of efficient management of video data. This management paved the way for new research areas, such as indexing and retrieval of video with respect to their spatio-temporal, visual and semantic contents. This thesis presents work towards a unified framework for semi-automated video indexing and interactive retrieval. To create an efficient index, a set of representative key frames are selected which capture and encapsulate the entire video content. This is achieved by, firstly, segmenting the video into its constituent ...

This doctoral thesis focuses on the development of algorithms of speaker characterisation by voice. Namely, characterisation of speaker’s identity, and the emotional state detectable in his voice while using the application of state-of-the art classifier algorithm Support Vector Machine (SVM) will be discussed. The first part deals with the state of the art SVM classifier utilised for classification experiments where we search for more sophisticated form of SVM model parameters selection. Also, we successfully apply optimization methods (PSO and GA algorithm) on two classification problems. The second part of this thesis deal with emotion recognition in continuous/dimensional space.

This thesis introduces new learning algorithms for Radial Basis Function (RBF) networks. RBF networks is a feed-forward two-layer neural network used for functional approximation or pattern classification applications. The proposed training algorithms are based on robust statistics. Their theoretical performance has been assessed and compared with that of classical algorithms for training RBF networks. The applications of RBF networks described in this thesis consist of simultaneously modeling moving object segmentation and optical flow estimation in image sequences and 3-D image modeling and segmentation. A Bayesian classifier model is used for the representation of the image sequence and 3-D images. This employs an energy based description of the probability functions involved. The energy functions are represented by RBF networks whose inputs are various features drawn from the images and whose outputs are objects. The hidden units embed kernel functions. Each kernel ...

Voice disorders need to be better understood because they may lead to reduced job chances and social isolation. Correct treatment indication and treatment effect measurements are needed to tackle these problems. They must rely on robust outcome measures for clinical intervention studies. Diplophonia is a severe and often misunderstood sign of voice disorders. Depending on its underlying etiology, diplophonic patients typically receive treatment such as logopedic therapy or phonosurgery. In the current clinical practice diplophonia is determined auditively by the medical doctor, which is problematic from the viewpoints of evidence-based medicine and scientific methodology. The aim of this thesis is to work towards objective (i.e., automatic) detection of diplophonia. A database of 40 euphonic, 40 diplophonic and 40 dysphonic subjects has been acquired. The collected material consists of laryngeal high-speed videos and simultaneous high-quality audio recordings. All material has been ...

Face recognition is one of the most challenging problems of computer vision and pattern recognition. The difficulty in face recognition arises mainly from facial appearance variations caused by factors, such as expression, illumination, partial face occlusion, and time gap between training and testing data capture. Moreover, the performance of face recognition algorithms heavily depends on prior facial feature localization step. That is, face images need to be aligned very well before they are fed into a face recognition algorithm, which requires precise facial feature localization. This thesis addresses on solving these two main problems -facial appearance variations due to changes in expression, illumination, occlusion, time gap, and imprecise face alignment due to mislocalized facial features- in order to accomplish its goal of building a generic face recognition algorithm that can function reliably under real-world conditions. The proposed face recognition algorithm ...

Over the last few years, i-vectors have been the state-of-the-art technique in speaker and language recognition. Recent advances in Deep Learning (DL) technology have improved the quality of i-vectors but the DL techniques in use are computationally expensive and need speaker or/and phonetic labels for the background data, which are not easily accessible in practice. On the other hand, the lack of speaker-labeled background data makes a big performance gap, in speaker recognition, between two well-known cosine and Probabilistic Linear Discriminant Analysis (PLDA) i-vector scoring techniques. It has recently been a challenge how to fill this gap without speaker labels, which are expensive in practice. Although some unsupervised clustering techniques are proposed to estimate the speaker labels, they cannot accurately estimate the labels. This thesis tries to solve the problems above by using the DL technology in different ways, without ...

Machine vision applies computer vision to industry and manufacturing in order to control or analyze a process or activity. Typical application of machine vision is the inspection of produced goods like electronic devices, automobiles, food and pharmaceuticals. Machine vision systems form their judgement based on specially designed image processing softwares. Therefore, image processing is very crucial for their accuracy. Food industry is among the industries that largely use image processing for inspection of produce. Fruits and vegetables have extremely varying physical appearance. Numerous defect types present for apples as well as high natural variability of their skin color brings apple fruits into the center of our interest. Traditional inspection of apple fruits is performed by human experts. But, automation of this process is necessary to reduce error, variation, fatigue and cost due to human experts as well as to increase ...

We present new results on single-channel speech separation and suggest a new separation approach to improve the speech quality of separated signals from an observed mix- ture. The key idea is to derive a mixture estimator based on sinusoidal parameters. The proposed estimator is aimed at finding sinusoidal parameters in the form of codevectors from vector quantization (VQ) codebooks pre-trained for speakers that, when combined, best fit the observed mixed signal. The selected codevectors are then used to reconstruct the recovered signals for the speakers in the mixture. Compared to the log- max mixture estimator used in binary masks and the Wiener filtering approach, it is observed that the proposed method achieves an acceptable perceptual speech quality with less cross- talk at different signal-to-signal ratios. Moreover, the method is independent of pitch estimates and reduces the computational complexity of the ...

Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications for intelligent human-computer interfaces. An important task is the automatic classification of non-verbal messages composed of facial signals where both facial expressions and head rotations are observed. This is a challenging task, because there is no definite grammar or code-book for mapping the non-verbal facial signals into a corresponding mental state. Furthermore, non-verbal facial signals and the observed emotions have dependency on personality, society, state of the mood and also the context in which they are displayed or observed. This thesis mainly addresses the three desired tasks for an effective visual information based automatic face and head gesture (FHG) analyzer. First we develop a fully automatic, robust and accurate 17-point facial landmark localizer based on local appearance information and structural information of ...

Voice biometric systems use automatic speaker verification (ASV) technology for user authentication. Even if it is among the most convenient means of biometric authentication, the robustness and security of ASV in the face of spoofing attacks (or presentation attacks) is of growing concern and is now well acknowledged by the research community. A spoofing attack involves illegitimate access to personal data of a targeted user. Replay is among the simplest attacks to mount - yet difficult to detect reliably and is the focus of this thesis. This research focuses on the analysis and design of existing and novel countermeasures for replay attack detection in ASV, organised in two major parts. The first part of the thesis investigates existing methods for spoofing detection from several perspectives. I first study the generalisability of hand-crafted features for replay detection that show promising results ...

Facial features segmentation, analysis and recognition of facial expressions by the Transferable Belief Model The aim of this work is the analysis and the classification of facial expressions. Experiments in psychology show that human is able to recognize the emotions based on the visualization of the temporal evolution of some characteristic fiducial points. Thus we firstly propose an automatic system for the extraction of the permanent facial features (eyes, eyebrows and lips). In this work we are interested in the problem of the segmentation of the eyes and the eyebrows. The segmentation of lips contours is based on a previous work developed in the laboratory. The proposed algorithm for eyes and eyebrows contours segmentation consists of three steps: firstly, the definition of parametric models to fit as accurate as possible the contour of each feature; then, a whole set of ...

The process of ageing causes changes to the voice over time. There have been significant research efforts in the automatic speaker recognition community towards improving performance in the presence of everyday variability. The influence of long-term variability, due to vocal ageing, has received only marginal attention however. In this Thesis, the impact of vocal ageing on speaker verification and forensic speaker recognition is assessed, and novel methods are proposed to counteract its effect. The Trinity College Dublin Speaker Ageing (TCDSA) database, compiled for this study, is first introduced. Containing 26 speakers, with recordings spanning an age difference of between 28 and 58 years per speaker, it is the largest longitudinal speech database in the public domain. A Gaussian Mixture Model-Universal Background Model (GMM-UBM) speaker verification experiment demonstrates a progressive decline in the scores of genuine-speakers as the age difference between ...

When exposed to noise, speakers will modify the way they speak in an effort to maintain intelligible communication. This process, which is referred to as Lombard effect (LE), involves a combination of both conscious and subconscious articulatory adjustment. Speech production variations due to LE can cause considerable degradation in automatic speech recognition (ASR) since they introduce a mismatch between parameters of the speech to be recognized and the ASR system’s acoustic models, which are usually trained on neutral speech. The main objective of this thesis is to analyze the impact of LE on speech production and to propose methods that increase ASR system performance in LE. All presented experiments were conducted on the Czech spoken language, yet, the proposed concepts are assumed applicable to other languages. The first part of the thesis focuses on the design and acquisition of a ...