No description available.

No description available.

People are able to respond more quickly to stimuli following a recent encounter with those same items. This facilitation in processing a stimulus as a function of a prior encounter is known as repetition priming. This thesis contains eleven experiments that attempt to describe the nature of the representations in memory underlying the effect. Changing aspects of the stimuli and task between the initial encounter and the subsequent test encounter allow examination of the kinds of information contained in these representations. The effects on repetition priming of recombining the pairings of simultaneously presented items in a speeded same/different matching task were examined (Experiments 1,2, 3 & 6). Despite priming being insensitive to the pairings of items, subjects were able to recognise this information (Experiment 4), revealing an item-specific locus for the priming effect and a dissociation from explicit memory. Priming was shown to be based on domain- specific and not conceptual information (Experiment 5). Experiments 7 and 8 examined the role of repeating the decision and response to stimuli in producing repetition priming. Results suggested that any such role reflects subjects' use of explicit memory as a basis for responding in the task, rather than reflecting automatic priming effects. Manipulating the semantic interpretation demanded of a homograph between training and test did not affect repetition priming (Experiment 9). The independent representation of simultaneously presented stimuli did not necessarily depend upon the activation of pre-existing representations or connections (Experiment 10). Experiment 11 revealed a necessary role for the identification of stimuli in producing repetition priming. The results of these eleven experiments pose problems for accounts of repetition priming based on the retrieval of processing episodes, or the reinstatement of processing demands. The results are consistent with a perceptual locus of the priming effect, based on representations of structure and form employed in the identification of individual stimuli.

The work in this thesis aims at studying the problems related to the robustness of a face recognition system where specific attention is given to the issues of handling the image variation complexity and inherent limited Unique Characteristic Information (UCI) within the scope of unfamiliar identity recognition environment. These issues will be the main themes in developing a mutual understanding of extraction and classification tasking strategies and are carried out as a two interdependent but related blocks of research work. Naturally, the complexity of the image variation problem is built up from factors including the viewing geometry, illumination, occlusion and other kind of intrinsic and extrinsic image variation. Ideally, the recognition performance will be increased whenever the variation is reduced and/or the UCI is increased. However, the variation reduction on 2D facial images may result in loss of important clues or UCI data for a particular face alternatively increasing the UCI may also increase the image variation. To reduce the lost of information, while reducing or compensating the variation complexity, a hybrid technique is proposed in this thesis. The technique is derived from three conventional approaches for the variation compensation and feature extraction tasks. In this first research block, transformation, modelling and compensation approaches are combined to deal with the variation complexity. The ultimate aim of this combination is to represent (transformation) the UCI without losing the important features by modelling and discard (compensation) and reduce the level of the variation complexity of a given face image. Experimental results have shown that discarding a certain obvious variation will enhance the desired information rather than sceptical in losing the interested UCI. The modelling and compensation stages will benefit both variation reduction and UCI enhancement. Colour, gray level and edge image information are used to manipulate the UCI which involve the analysis on the skin colour, facial texture and features measurement respectively. The Derivative Linear Binary transformation (DLBT) technique is proposed for the features measurement consistency. Prior knowledge of input image with symmetrical properties, the informative region and consistency of some features will be fully utilized in preserving the UCI feature information. As a result, the similarity and dissimilarity representation for identity parameters or classes are obtained from the selected UCI representation which involves the derivative features size and distance measurement, facial texture and skin colour. These are mainly used to accommodate the strategy of unfamiliar identity classification in the second block of the research work. Since all faces share similar structure, classification technique should be able to increase the similarities within the class while increase the dissimilarity between the classes. Furthermore, a smaller class will result on less burden on the identification or recognition processes. The proposed method or collateral classification strategy of identity representation introduced in this thesis is by manipulating the availability of the collateral UCI for classifying the identity parameters of regional appearance, gender and age classes. In this regard, the registration of collateral UCI s have been made in such a way to collect more identity information. As a result, the performance of unfamiliar identity recognition positively is upgraded with respect to the special UCI for the class recognition and possibly with the small size of the class. The experiment was done using data from our developed database and open database comprising three different regional appearances, two different age groups and two different genders and is incorporated with pose and illumination image variations.

No description available.

No description available.

Current automatic speech recognition (ASR) research is focused on recognition of continuous, spontaneous speech. Spontaneous speech contains a lot of variability in the way words are pronounced, and canonical pronunciations of each word are not true to the variation that is seen in real data. Two of the components of an ASR system are acoustic models and pronunciation models. The variation within spontaneous speech must be accounted for by these components. Phones, or context-dependent phones are typically used as the base subword unit, and one acoustic model is trained for each sub-word unit. Pronunciation modelling largely takes place in a dictionary, which relates words to sequences of phones. Acoustic modelling and pronunciation modelling overlap, and the two are not clearly separable in modelling pronunciation variation. Techniques that find pronunciation variants in the data and then reflect these in the dictionary have not provided expected gains in recognition. An alternative approach to modelling pronunciations in terms of phones is to derive units automatically: using data-driven methods to determine an inventory of sub-word units, their acoustic models, and their relationship to words. This thesis presents a method for the automatic derivation of a sub-word unit inventory, whose main components are 1. automatic and simultaneous generation of a sub-word unit inventory and acoustic model set, using an ergodic hidden Markov model whose complexity is controlled using the Bayesian Information Criterion 2. automatic generation of probabilistic dictionaries using joint multigrams The prerequisites of this approach are fewer than in previous work on unit derivation; notably, the timings of word boundaries are not required here. The approach is language independent since it is entirely data-driven and no linguistic information is required. The dictionary generation method outperforms a supervised method using phonetic data. The automatically derived units and dictionary perform reasonably on a small spontaneous speech task, although not yet outperforming phones.

Speech recognition has been a very active area of research over the past twenty years. Despite an evident progress, it is generally agreed by the practitioners of the field that performance of the current speech recognition systems is rather suboptimal and new approaches are needed. The motivation behind the undertaken research is an observation that the notion of representation of objects and concepts that once was considered to be central in the early days of pattern recognition, has been largely marginalised by the advent of statistical approaches. As a consequence of a predominantly statistical approach to speech recognition problem, due to the numeric, feature vector-based, nature of representation, the classes inductively discovered from real data using decision-theoretic techniques have little meaning outside the statistical framework. This is because decision surfaces or probability distributions are difficult to analyse linguistically. Because of the later limitation it is doubtful that the gap between speech recognition and linguistic research can be bridged by the numeric representations. This thesis investigates an alternative, structural, approach to spoken language representation and categorisation. The approach pursued in this thesis is based on a consistent program, known as the Evolving Transformation System (ETS), motivated by the development and clarification of the concept of structural representation in pattern recognition and artificial intelligence from both theoretical and applied points of view. This thesis consists of two parts. In the first part of this thesis, a similarity-based approach to structural representation of speech is presented. First, a linguistically well-motivated structural representation of phones based on distinctive phonological features recovered from speech is proposed. The representation consists of string templates representing phones together with a similarity measure. The set of phonological templates together with a similarity measure defines a symbolic metric space. Representation and ETS-inspired categorisation in the symbolic metric spaces corresponding to the phonological structural representation are then investigated by constructing appropriate symbolic space classifiers and evaluating them on a standard corpus of read speech. In addition, similarity-based isometric transition from phonological symbolic metric spaces to the corresponding non-Euclidean vector spaces is investigated. Second part of this thesis deals with the formal approach to structural representation of spoken language. Unlike the approach adopted in the first part of this thesis, the representation developed in the second part is based on the mathematical language of the ETS formalism. This formalism has been specifically developed for structural modelling of dynamic processes. In particular, it allows the representation of both objects and classes in a uniform event-based hierarchical framework. In this thesis, the latter property of the formalism allows the adoption of a more physiologically-concreteapproach to structural representation. The proposed representation is based on gestural structures and encapsulates speech processes at the articulatory level. Algorithms for deriving the articulatory structures from the data are presented and evaluated.

The thesis reports six experiments investigating word recognition and reading in Arabic. Experiment 1 looked at the word superiority effect in Arabic word recognition using brief presentations of stimuli (five-letter real words, pseudo-words, non-words, and inverted real words) in a Reicher-Wheeler task. The results of this experiment showed advantages for the recognition of words over pseudo-words and illegal non-words, and for pseudo-words over illegal non-words. Experiment 2 was a follow-up experiment that also examined the word superiority effect in Arabic by using the lexical decision task. In this experiment, participants viewed briefly presented real words and legal non-words, with the results showing that Arabic real words were recognised quicker and more accurately than non-words. Experiment 3 investigated the landing position effects for three, five, and seven letter words in Arabic using eye movements while reading. The results showed that the preferred viewing location (PVL) is at the right of centre of words in Arabic, similar to that for Hebrew. Experiment 4 re-examined the optimal viewing position in Arabic word recognition using five-letter Arabic words and non-words in a lexical decision task. The results showed that participants recognised words most quickly and most accurately when fixating inter-letter locations at the middle of words, indicating that the OVP for Arabic word recognition is at a word’s centre. Experiment 5 used the Reicher-Wheeler task and Experiment 6 used the lexical decision task to re-examine the claim that an anatomical division in the human fovea has consequences for word recognition. The findings revealed the superiority of the right visual field for words displayed outside the foveal and no asymmetries for words displayed within foveal vision. Thus far the research has made an important advance on our understanding of processes involved in Arabic word recognition by revealing that word superiority and pseudo-word superiority effects similar to those reported in Latinate languages are also observed in Arabic, and that the OVP effect in Arabic differs from that found in English. The reading results indicate that, similar to other languages, parafoveal word length information is used to guide saccade targeting in Arabic.

This thesis examines the role of emotion in face recognition, using measures of the visual scanpath as indicators of recognition. There are two key influences of emotion in face recognition: the emotional expression displayed upon a face, and the emotional feelings evoked within a perceiver in response to a familiar person. An initial set of studies examined these processes in healthy participants. First, positive emotional expressions were found to facilitate the processing of famous faces, and negative expressions facilitated the processing of novel faces. A second set of studies examined the role of emotional feelings in recognition. Positive feelings towards a face were also found to facilitate processing, in both an experimental study using newly learned faces and in the recognition of famous faces. A third set of studies using healthy participants examined the relative influences of emotional expression and emotional feelings in face recognition. For newly learned faces, positive expressions and positive feelings had a similar influence in recognition, with no presiding role of either dimension. However, emotional feelings had an influence over and above that of expression in the recognition of famous faces. A final study examined whether emotional valence could influence covert recognition in developmental prosopagnosia, and results suggested the patients process faces according to emotional valence rather than familiarity per se. Specifically, processing was facilitated for studied-positive faces compared to studied-neutral and novel faces, but impeded for studied-negative faces. This pattern of findings extends existing reports of a positive-facilitation effect in face recognition, and suggests there may be a closer relationship between facial familiarity and emotional valence than previously envisaged. The implications of these findings are discussed in relation to models of normal face recognition and theories of covert recognition in prosopagnosia.