Multimodal Fusion of Polynomial Classifiers for Automatic Person Recognition

With the prevalence of the information age, privacy and personalization are forefront in today\u27s society. As such, biometrics are viewed as essential components of current and evolving technological systems. Consumers demand unobtrusive and noninvasive approaches. In our previous work, we have demonstrated a speaker verification system that meets these criteria. However, there are additional constraints for fielded systems. The required recognition transactions are often performed in adverse environments and across diverse populations, necessitating robust solutions. There are two significant problem areas in current generation speaker verification systems. The first is the difficulty in acquiring clean audio signals (in all environments) without encumbering the user with a head-mounted close-talking microphone. Second, unimodal biometric systems do not work with a significant percentage of the population. To combat these issues, multimodal techniques are being investigated to improve system robustness to environmental conditions, as well as improve overall accuracy across the population. We propose a multimodal approach that builds on our current state-of-the-art speaker verification technology. In order to maintain the transparent nature of the speech interface, we focus on optical sensing technology to provide the additional modality–giving us an audio-visual person recognition system. For the audio domain, we use our existing speaker verification system. For the visual domain, we focus on lip motion. This is chosen, rather than static face or iris recognition, because it provides dynamic information about the individual. In addition, the lip dynamics can aid speech recognition to provide liveness testing. The visual processing method makes use of both color and edge information, combined within a Markov random field (MRF) framework, to localize the lips. Geometric features are extracted and input to a polynomial classifier for the person recognition process. A late integration approach, based on a probabilistic model, is employed to combine the two modalities. The system is tested on the XM2VTS database combined with AWGN (in the audio domain) over a range of signal-to-noise ratios

Face Detection And Lip Localization

Integration of audio and video signals for automatic speech recognition has become an important field of study. The Audio-Visual Speech Recognition (AVSR) system is known to have accuracy higher than audio-only or visual-only system. The research focused on the visual front end and has been centered around lip segmentation. Experiments performed for lip feature extraction were mainly done in constrained environment with controlled background noise. In this thesis we focus our attention to a database collected in the environment of a moving car which hampered the quality of the imagery. We first introduce the concept of illumination compensation, where we try to reduce the dependency of light from over- or under-exposed images. As a precursor to lip segmentation, we focus on a robust face detection technique which reaches an accuracy of 95%. We have detailed and compared three different face detection techniques and found a successful way of concatenating them in order to increase the overall accuracy. One of the detection techniques used was the object detection algorithm proposed by Viola-Jones. We have experimented with different color spaces using the Viola-Jones algorithm and have reached interesting conclusions. Following face detection we implement a lip localization algorithm based on the vertical gradients of hybrid equations of color. Despite the challenging background and image quality, success rate of 88% was achieved for lip segmentation

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems

Lip Feature Extraction Towards an Automatic Speechreading System

The use of color information can significantly improve efficiency and robustness of lip feature extraction capability over purely grayscale-based methods. Edge information provides another useful tool in characterizing lip boundaries. In this paper we present a method of integrating both types of information to address the problem of lip feature extraction for the purpose of speechreading. We first examine various color models and view hue as an effective descriptor to characterize the lips due to its invariance to luminance and human skin color, and its discriminative properties. We use prominent red hue as an indicator to locate the position of the lips. Based on the identified lip area, we further refine the interior and exterior lip boundary using both color and spatial edge information, where those two are combined within a Markov random field (MRF) framework. Experimental results are presented to show the effectiveness of this method

Investigating spoken emotion : the interplay of language and facial expression

This thesis aims to investigate how spoken expressions of emotions are influenced by the characteristics of spoken language and the facial emotion expression. The first three chapters examined how production and perception of emotions differed between Cantonese (tone language) and English (non-tone language). The rationale for this contrast was that the acoustic property of Fundamental Frequency (F0) may be used differently in the production and perception of spoken expressions in tone languages as F0 may be preserved as a linguistic resource for the production of lexical tones. To test this idea, I first developed the Cantonese Audio-visual Emotional Speech (CAVES) database, which was then used as stimuli in all the studies presented in this thesis (Chapter 1). An emotion perception study was then conducted to examine how three groups of participants (Australian English, Malaysian Malay and Hong Kong Cantonese speakers) identified spoken expression of emotions that were produced in either English or Cantonese (Chapter 2). As one of the aims of this study was to disambiguate the effects of language from culture, these participants were selected on the basis that they either shared similarities in language type (non-tone language, Malay and English) or culture (collectivist culture, Cantonese and Malay). The results showed that a greater similarity in emotion perception was observed between those who spoke a similar type of language, as opposed to those who shared a similar culture. This suggests some intergroup differences in emotion perception may be attributable to cross-language differences. Following up on these findings, an acoustic analysis study (Chapter 3) showed that compared to English spoken expression of emotions, Cantonese expressions had less F0 related cues (median and flatter F0 contour) and also the use of F0 cues was different. Taken together, these results show that language characteristics (n F0 usage) interact with the production and perception of spoken expression of emotions. The expression of disgust was used to investigate how facial expressions of emotions affect speech articulation. The rationale for selecting disgust was that the facial expression of disgust involves changes to the mouth region such as closure and retraction of the lips, and these changes are likely to have an impact on speech articulation. To test this idea, an automatic lip segmentation and measurement algorithm was developed to quantify the configuration of the lips from images (Chapter 5). By comparing neutral to disgust expressive speech, the results showed that disgust expressive speech is produced with significantly smaller vertical mouth opening, greater horizontal mouth opening and lower first and second formant frequencies (F1 and F2). Overall, this thesis provides an insight into how aspects of expressive speech may be shaped by specific (language type) and universal (face emotion expression) factors

A motion based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Doctor of Philosophy. Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with the resulting loss of speech. With recent advances in portable computing power, automatic lip-reading (ALR) may become a viable approach to voice restoration. This thesis addresses the image processing aspect of ALR, and focuses three contributions to colour-based lip segmentation. The rst contribution concerns the colour transform to enhance the contrast between the lips and skin. This thesis presents the most comprehensive study to date by measuring the overlap between lip and skin histograms for 33 di erent colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%, and results show that selecting the correct transform can increase the segmentation accuracy by up to three times. The second contribution is the development of a new lip segmentation algorithm that utilises the best colour transforms from the comparative study. The algorithm is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation error (SE) of 7:39 %. The third contribution focuses on the impact of the histogram threshold on the segmentation accuracy, and introduces a novel technique called Adaptive Threshold Optimisation (ATO) to select a better threshold value. The rst stage of ATO incorporates -SVR to train the lip shape model. ATO then uses feedback of shape information to validate and optimise the threshold. After applying ATO, the SE decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp or relative improvement of 15:1%. While this thesis concerns lip segmentation in particular, ATO is a threshold selection technique that can be used in various segmentation applications.MT201

Robust visual speech recognition using optical flow analysis and rotation invariant features

The focus of this thesis is to develop computer vision algorithms for visual speech recognition system to identify the visemes. The majority of existing speech recognition systems is based on audio-visual signals and has been developed for speech enhancement and is prone to acoustic noise. Considering this problem, aim of this research is to investigate and develop a visual only speech recognition system which should be suitable for noisy environments. Potential applications of such a system include the lip-reading mobile phones, human computer interface (HCI) for mobility-impaired users, robotics, surveillance, improvement of speech based computer control in a noisy environment and for the rehabilitation of the persons who have undergone a laryngectomy surgery. In the literature, there are several models and algorithms available for visual feature extraction. These features are extracted from static mouth images and characterized as appearance and shape based features. However, these methods rarely incorporate the time dependent information of mouth dynamics. This dissertation presents two optical flow based approaches of visual feature extraction, which capture the mouth motions in an image sequence. The motivation for using motion features is, because the human perception of lip-reading is concerned with the temporal dynamics of mouth motion. The first approach is based on extraction of features from the optical flow vertical component. The optical flow vertical component is decomposed into multiple non-overlapping fixed scale blocks and statistical features of each block are computed for successive video frames of an utterance. To overcome the issue of large variation in speed of speech, each utterance is normalized using simple linear interpolation method. In the second approach, four directional motion templates based on optical flow are developed, each representing the consolidated motion information in an utterance in four directions (i.e.,up, down, left and right). This approach is an evolution of a view based approach known as motion history image (MHI). One of the main issues with the MHI method is its motion overwriting problem because of self-occlusion. DMHIs seem to solve this issue of overwriting. Two types of image descriptors, Zernike moments and Hu moments are used to represent each image of DMHIs. A support vector machine (SVM) classifier was used to classify the features obtained from the optical flow vertical component, Zernike and Hu moments separately. For identification of visemes, a multiclass SVM approach was employed. A video speech corpus of seven subjects was used for evaluating the efficiency of the proposed methods for lip-reading. The experimental results demonstrate the promising performance of the optical flow based mouth movement representations. Performance comparison between DMHI and MHI based on Zernike moments, shows that the DMHI technique outperforms the MHI technique. A video based adhoc temporal segmentation method is proposed in the thesis for isolated utterances. It has been used to detect the start and the end frame of an utterance from an image sequence. The technique is based on a pair-wise pixel comparison method. The efficiency of the proposed technique was tested on the available data set with short pauses between each utterance

Towards An Intelligent Fuzzy Based Multimodal Two Stage Speech Enhancement System

This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded. In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments. Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks are made along with proposals for future work