Persuading the Public : A Linguistic Analysis of Barack Obama’s Speech on “Super Tuesday” 2008

This essay examines the persuasive side of language in a speech given by Senator Barack Obama on Super Tuesday in February 2008. It studies how Senator Obama utilizes language to convince and persuade his audience. This is done from an Aristotelian point of view, meaning that the study focuses foremost on how the senator’s word choices relate to Aristotle’s three means of persuasion, ethos, pathos and logos. Those basic guiding principles are relevant to use since Aristotle’s work on the subject of rhetoric is still today one of the most relevant works in that field. The analysis is basically performed through personal observations guided by previous studies, within the frame of Aristotelian rhetoric. The results show how Senator Obama enforces the three means of persuasion through language and how it can be considered persuasive. The study might add to rhetoric studies from a linguistic perspective since it reaches a better understanding of language used in the field of politics, where rhetoric is a prominent component.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Autoregressive decomposition and pole tracking applied to vocal fold nodule signals

This letter describes a novel algorithm that is based on autoregressive decomposition and pole tracking used to recognize two patterns of speech data: normal voice and disphonic voice caused by nodules. The presented method relates the poles and the peaks of the signal spectrum which represent the periodic components of the voice. The results show that the perturbation contained in the signal is clearly depicted by pole's positions. Their variability is related to jitter and shimmer. The pole dispersion for pathological voices is about 20% higher than for normal voices, therefore, the proposed approach is a more trustworthy measure than the classical ones. © 2007.

A Cognitive Science Reasoning in Recognition of Emotions in Audio-Visual Speech

In this report we summarize the state-of-the-art of speech emotion recognition from the signal processing point of view. On the bases of multi-corporal experiments with machine-learning classifiers, the observation is made that existing approaches for supervised machine learning lead to database dependent classifiers which can not be applied for multi-language speech emotion recognition without additional training because they discriminate the emotion classes following the used training language. As there are experimental results showing that Humans can perform language independent categorisation, we made a parallel between machine recognition and the cognitive process and tried to discover the sources of these divergent results. The analysis suggests that the main difference is that the speech perception allows extraction of language independent features although language dependent features are incorporated in all levels of the speech signal and play as a strong discriminative function in human perception. Based on several results in related domains, we have suggested that in addition, the cognitive process of emotion-recognition is based on categorisation, assisted by some hierarchical structure of the emotional categories, existing in the cognitive space of all humans. We propose a strategy for developing language independent machine emotion recognition, related to the identification of language independent speech features and the use of additional information from visual (expression) features.

Evaluation of oral-motor movements and speech in patients with tetanus of a public service in Brazil

The characterisation of oral-motor movements and speech of patients with tetanus were investigated to determine the existence of possible signs that are characteristic of this pathology. Thirteen patients clinically diagnosed with tetanus (10 with severe tetanus and three with very severe tetanus) and admitted to an intensive care unit underwent clinical evaluation of oral-motor movements and speech. Statistical analysis indicated significant between-group differences for speech motor functions, suggesting that individuals with very severe tetanus present rigidity as a characteristic interfering in articulatory precision (P = 0 035) and movement rate (P = 0 038). For lip closure, tongue movement, palatal elevation, gag reflex and voice quality, no between-group differences were identified for the specific abnormal characteristics. The observed abnormal results indicate that muscle strength and functional status of the oral-motor system presented by most of the participants of the study did not ensure the necessary integrity for satisfactory performance. The characterisation of the oral myofunctional aspects of patients with tetanus provides medical teams, patients and families with a wider and better description of the clinical situation, giving support to the diagnosis, prognostics and treatment.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Analysis of financial data series using fractional Fourier transform and multidimensional scaling

The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000–2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work.

Evaluation of the effect of hearing aids using electroencephalographic analysis

based on the report for the Doctoral Conference of the PhD programme in Technology Assessment, held at FCT-UNL Campus, Monte de Caparica, July 2013. The PhD thesis has the supervision of Dr. Salomé Almeida (Central Hospital of Lisbon), and co-supervision of Prof. Manuel Ortigueira (FCT-UNL).

Automatic DVB signal analyser

El problema de controlar les emissions de televisió digital a tota Europa pel desenvolupament de receptors robustos i fiables és cada vegada més significant, per això, sorgeix la necessitat d’automatitzar el procés d’anàlisi i control d’aquests senyals. Aquest projecte presenta el desenvolupament software d’una aplicació que vol solucionar una part d’aquest problema. L’aplicació s’encarrega d’analitzar, gestionar i capturar senyals de televisió digital. Aquest document fa una introducció a la matèria central que és la televisió digital i la informació que porten els senyals de televisió, concretament, la que es refereix a l’estàndard "Digital Video Broadcasting". A continuació d’aquesta part, l’escrit es concentra en l’explicació i descripció de les funcionalitats que necessita cobrir l'aplicació, així com introduir i explicar cada etapa d’un procés de desenvolupament software. Finalment, es resumeixen els avantatges de la creació d’aquest programa per l’automatització de l’anàlisi de senyal digital partint d’una optimització de recursos.

Neuronal responses in mouse barrel cortex:Comparison of two signal discriminators

Barrels are discrete cytoarchitectonic neurons cluster located in the layer IV of the somatosensory¦cortex in mice brain. Each barrel is related to a specific whisker located on the mouse snout. The¦whisker-to-barrel pathway is a part of the somatosensory system that is intensively used to explore¦sensory activation induced plasticity in the cerebral cortex.¦Different recording methods exist to explore the cortical response induced by whisker deflection in¦the cortex of anesthetized mice. In this work, we used a method called the Single-Unit Analysis by¦which we recorded the extracellular electric signals of a single barrel neuron using a microelectrode.¦After recording the signal was processed by discriminators to isolate specific neuronal shape (action¦potentials).¦The objective of this thesis was to familiarize with the barrel cortex recording during whisker¦deflection and its theoretical background and to compare two different ways of discriminating and¦sorting cortical signal, the Waveform Window Discriminator (WWD) or the Spike Shape Discriminator (SSD).¦WWD is an electric module allowing the selection of specific electric signal shape. A trigger and a¦window potential level are set manually. During measurements, every time the electric signal passes¦through the two levels a dot is generated on time line. It was the method used in previous¦extracellular recording study in the Département de Biologie Cellulaire et de Morphologie (DBCM) in¦Lausanne.¦SSD is a function provided by the signal analysis software Spike2 (Cambridge Electronic Design). The¦neuronal signal is discriminated by a complex algorithm allowing the creation of specific templates.¦Each of these templates is supposed to correspond to a cell response profile. The templates are saved¦as a number of points (62 in this study) and are set for each new cortical location. During¦measurements, every time the cortical recorded signal corresponds to a defined number of templates¦points (60% in this study) a dot is generated on time line. The advantage of the SSD is that multiple¦templates can be used during a single stimulation, allowing a simultaneous recording of multiple¦signals.¦It exists different ways to represent data after discrimination and sorting. The most commonly used¦in the Single-Unit Analysis of the barrel cortex are the representation of the time between stimulation¦and the first cell response (the latency), the representation of the Response Magnitude (RM) after¦whisker deflection corrected for spontaneous activity and the representation of the time distribution¦of neuronal spikes on time axis after whisker stimulation (Peri-Stimulus Time Histogram, PSTH).¦The results show that the RMs and the latencies in layer IV were significantly different between the¦WWD and the SSD discriminated signal. The temporal distribution of the latencies shows that the¦different values were included between 6 and 60ms with no peak value for SSD while the WWD¦data were all gathered around a peak of 11ms (corresponding to previous studies). The scattered¦distribution of the latencies recorded with the SSD did not correspond to a cell response.¦The SSD appears to be a powerful tool for signal sorting but we do not succeed to use it for the¦Single-Unit Analysis extracellular recordings. Further recordings with different SSD templates settings¦and larger sample size may help to show the utility of this tool in Single-Unit Analysis studies.

EEG Windowed statitical wavelet deviation for estimation of muscular artifacts

Electroencephalographic (EEG) recordings are, most of the times, corrupted by spurious artifacts, which should be rejected or cleaned by the practitioner. As human scalp EEG screening is error-prone, automatic artifact detection is an issue of capital importance, to ensure objective and reliable results. In this paper we propose a new approach for discrimination of muscular activity in the human scalp quantitative EEG (QEEG), based on the time-frequency shape analysis. The impact of the muscular activity on the EEG can be evaluated from this methodology. We present an application of this scoring as a preprocessing step for EEG signal analysis, in order to evaluate the amount of muscular activity for two set of EEG recordings for dementia patients with early stage of Alzheimer’s disease and control age-matched subjects.

Modified Block Adaptive Predictive Coder For Speech Processing

This thesis investigated the potential use of Linear Predictive Coding in speech communication applications. A Modified Block Adaptive Predictive Coder is developed, which reduces the computational burden and complexity without sacrificing the speech quality, as compared to the conventional adaptive predictive coding (APC) system. For this, changes in the evaluation methods have been evolved. This method is as different from the usual APC system in that the difference between the true and the predicted value is not transmitted. This allows the replacement of the high order predictor in the transmitter section of a predictive coding system, by a simple delay unit, which makes the transmitter quite simple. Also, the block length used in the processing of the speech signal is adjusted relative to the pitch period of the signal being processed rather than choosing a constant length as hitherto done by other researchers. The efficiency of the newly proposed coder has been supported with results of computer simulation using real speech data. Three methods for voiced/unvoiced/silent/transition classification have been presented. The first one is based on energy, zerocrossing rate and the periodicity of the waveform. The second method uses normalised correlation coefficient as the main parameter, while the third method utilizes a pitch-dependent correlation factor. The third algorithm which gives the minimum error probability has been chosen in a later chapter to design the modified coder The thesis also presents a comparazive study beh-cm the autocorrelation and the covariance methods used in the evaluaiicn of the predictor parameters. It has been proved that the azztocorrelation method is superior to the covariance method with respect to the filter stabf-it)‘ and also in an SNR sense, though the increase in gain is only small. The Modified Block Adaptive Coder applies a switching from pitch precitzion to spectrum prediction when the speech segment changes from a voiced or transition region to an unvoiced region. The experiments cont;-:ted in coding, transmission and simulation, used speech samples from .\£=_‘ajr2_1a:r1 and English phrases. Proposal for a speaker reecgnifion syste: and a phoneme identification system has also been outlized towards the end of the thesis.

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.