This dissertation addresses the general problem of recognition of acoustic signals which may be derived from speech, sonar, or acoustic phenomena. The specific problem of recognizing speech is the main focus of this research. The intention is to design a recognition system for a definite number of discrete words. For this purpose specifically, eight isolated words from the T1MIT database are selected. Four medium length words "greasy," "dark," "wash," and "water" are used. In addition, four short words are considered "she," "had," "in," and "all." The recognition system addresses the following issues: filtering or preprocessing, training, and decision-making. The preprocessing phase uses linear predictive coding of order 12. Following the filtering process, a vector quantization method is used to further reduce the input data and generate a finite inductive sequence of symbols representative of each input signal. The sequences generated by the vector quantization process of the same word are factored, and a single ruling or reference template is generated and stored in a codebook. This system introduces a new modeling technique which relies heavily on the basic concept that all finite sequences are finitely inductive. This technique is used in the training stage. In order to accommodate the variabilities in speech, the training is performed casualty, and a large number of training speakers is used from eight different dialect regions. Hence, a speaker independent recognition system is realized. The matching process compares the incoming speech with each of the templates stored, and a closeness ration is computed. A ratio table is generated anH the matching word that corresponds to the smallest ratio (i.e. indicating that the ruling has removed most of the symbols) is selected. Promising results were obtained for isolated words, and the recognition rates ranged between 50% and 100%.

This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions.

Thesis advisor: Liane L. Young / Thesis advisor: Elizabeth A. Kensinger / Theory of mind refers to the process of representing others’ mental states. This process consistently elicits activity in a network of brain regions: the theory of mind network (ToMN). Typically, theory of mind has been understood in terms of content, i.e. representing the semantic content of someone’s beliefs. However, recent work has proposed that ToMN activity could be better understood in the context of social prediction; or, more specifically, prediction error—the difference between observed and predicted information. Social predictions can be represented in multiple forms—e.g. dispositional predictions about who a person is, prescriptive norms about what people should do, and descriptive norms about what people frequently do. Part 1 examined the relationship between social prediction error and ToMN activity, finding that the activity in the ToMN was related to both dispositional, and prescriptive predictions. Part 2 examined the semantic content represented by moral claims. Prior work has suggested that morals are generally represented and understood as objective, i.e. akin to facts. Instead, we found that moral claims are represented as far more social than prior work had anticipated, eliciting a great deal of activity across the ToMN. Part 3 examined the relationship between ToMN activity and metaethical status, i.e. the extent that morals were perceived as objective or subjective. Objective moral claims elicited less ToMN activity, whereas subjective moral claimed elicited more. We argue that this relationship is best understood in the context of prediction, where objective moral claims represent strong social priors about what most people will believe. Finally, I expand on this finding and argue that a theoretical approach incorporating social prediction has serious implications for morality, or more specifically, for the motivations underlying normative compliance. People may be compelled to observe moral rules because doing so maintains a predictable social environment. / Thesis (PhD) — Boston College, 2017. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology.

According to the predictive coding approach to perception, the brain uses predictions based on previous experience to optimise perception, by allocating more computational resources to important or unexpected stimuli. Overall, predictions allow faster and more accurate recognition, but occasionally, when the prediction is incorrect, it may lead to a misperception. The aim of this thesis was to investigate the influence of top - down processes on perceptual decisions. I utilised misperceptions as a signature of those top - down influences and Signal Detection Theory to assess their size, type and direction. I used Electroencephalography to determine the stage of information processing at which different types of predictions influence sensory processing.The empirical studies are clustered around Topic 1: Influence of Predictions on Perception, Topic 2: Types of Predictions and Topic 3: Value as Modulator of Perception.Studies clustered in Topic 1 analysed and quantified the influence of predictions on perceptual decisions and showed that misperceptions can be triggered by wrong predictions only in very specific circumstances. In particular, misperceptions occurred only if there was some degree of correspondence between the wrong prediction and the sensory input. Otherwise, predictions were easily rejected, increasing the overall accuracy. I also demonstrated that misperceptions were most likely to happen in a window on the continuum of input quality where the stimulus - related uncertainty was highest. Topic 2 comprised experiments investigating different types of predictions and their interaction. Behavioural (but not EEG) results revealed interference between passive and active expectations. The early event related (ERP) components N1 and P2, as well as the P300, were all modulated by expectations. Expected events either increased or decreased the P300 amplitude, depending on whether the expected item was predictable and thus ignored, or awaited and thus flagged for further processing. This suggests that P300 might be an index of top - down resource allocation. Experiments within Topic 3 studied the influence of values, as examples of executive processes, on perceptual decisions, using either natural or acquired high - value stimuli. The results suggested that the process of recognition is adjusted in a top - down manner to account for the cost and benefit values related to different outcomes. The trade - off between processing time and accuracy is not fixed, but can be adjusted to optimise recognition in the task at hand. Furthermore, value can change the focus of perception, resulting in different elements of the sensory input being amplified or ignored. Overall, these results showed that misperceptions are 'intelligent mistakes' - a by - product of a top-down, prediction - based optimisation strategy that decreases the computational load, while increasing accuracy and improving the allocation of computational resources.

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In this thesis, the red hot topic anomaly detection is studied, which is a subtopic in machine learning. The company, Procera Networks, supports several broadband companies with IT-solutions and would like to detected errors in these systems automatically. This thesis investigates and devises methods and algorithms for detecting interesting events in diagnostics data. Events of interest include: short-term deviations (a deviating point), long-term deviations (a distinct trend) and other unexpected deviations. Three models are analyzed, namely Linear Predictive Coding, Sparse Linear Prediction and Wavelet Transformation. The final outcome is determined by the gap to certain thresholds. These thresholds are customized to fit the model as well as possible. / I den här rapporten kommer det glödheta området anomalidetektering studeras, vilket tillhör ämnet Machine Learning. Företaget där arbetet utfördes på heter Procera Networks och jobbar med IT-lösningar inom bredband till andra företag. Procera önskar att kunna upptäcka fel hos kunderna i dessa system automatiskt. I det här projektet kommer olika metoder för att hitta intressanta företeelser i datatraffiken att genomföras och forskas kring. De mest intressanta företeelserna är framfärallt snabba avvikelser (avvikande punkt) och färändringar äver tid (trender) men också andra oväntade mänster. Tre modeller har analyserats, nämligen Linear Predictive Coding, Sparse Linear Prediction och Wavelet Transform. Det slutgiltiga resultatet från modellerna är grundat på en speciell träskel som är skapad fär att ge ett så bra resultat som mäjligt till den undersäkta modellen..

I outline and defend a theory of mental representation based on three ideas that I extract from the work of the mid-twentieth century philosopher, psychologist, and cybernetician Kenneth Craik: first, an account of mental representation in terms of idealised models that capitalize on structural similarity to their targets; second, an appreciation of prediction as the core function of such models; and third, a regulatory understanding of brain function. I clarify and elaborate on each of these ideas, relate them to contemporary advances in neuroscience and machine learning, and favourably contrast a predictive model-based theory of mental representation with other prominent accounts of the nature, importance, and functions of mental representations in cognitive science and philosophy.

Résumé indisponible. / Résumé indisponible.

A multi-language identification system of 10 languages: Mandarin, Japanese, Korean, Tamil, Vietnamese, English, French, German, Spanish and Farsi, is built in this thesis. The system utilizes cepstrum coefficients, delta cepstrum coefficients and linear predictive coding coefficients to extract the language features, and incorporates Gaussian mixture model and N-gram model to make the language classification. The feasibility of the system is demonstrated in this thesis.

This dissertation develops computational models to address outstanding problems in the domain of expectation-related cognitive processes and their neuroimaging markers in functional MRI or EEG. The new models reveal a way to unite diverse phenomena within a common framework focused on dynamic neural encoding shifts, which can arise from robust interactive effects of M-currents and chloride currents in pyramidal neurons. By specifying efficient, biologically realistic circuits that achieve predictive coding (e.g., Friston, 2005), these models bridge among neuronal biophysics, systems neuroscience, and theories of cognition. Chapter one surveys data types and neural processes to be examined, and outlines the Dynamically Labeled Predictive Coding (DLPC) framework developed during the research. Chapter two models hippocampal prediction and mismatch, using the DLPC framework. Chapter three presents extensions to the model that allow its application for modeling neocortical EEG genesis. Simulations of this extended model illustrate how dynamic encoding shifts can produce Mismatch Negativity (MMN) phenomena, including pharmacological effects on MMN reported for humans or animals. Chapters four and five describe new modeling studies of possible neural bases for alpha-induced information suppression, a phenomenon associated with active ignoring of stimuli. Two models explore the hypothesis that in simple rate-based circuits, information suppression might be a robust effect of neural saturation states arising near peaks of resonant alpha oscillations. A new proposal is also introduced for how the basal ganglia may control onset and offset of alpha-induced information suppression. Although these rate models could reproduce many experimental findings, they fell short of reproducing a key electrophysiological finding: phase-dependent reduction in spiking activity correlated with power in the alpha frequency band. Therefore, chapter five also specifies how a DLPC model, adapted from the neocortical model developed in chapter three, can provide an expectation-based model of alpha-induced information suppression that exhibits phase-dependent spike reduction during alpha-band oscillations. The model thus can explain experimental findings that were not reproduced by the rate models. The final chapter summarizes main theses, results, and basic research implications, then suggests future directions, including expanded models of neocortical mismatch, applications to artificial neural networks, and the introduction of reward circuitry.