Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Combined marrow stromal cell-sheet techniques and high-strength biodegradable composite scaffolds for engineered functional bone grafts

In this study, cell sheets comprising multilayered porcine bone marrow stromal cells (BMSC) were assembled with fully interconnected scaffolds made from medical-grade polycaprolactone–calcium phosphate (mPCL–CaP), for the engineering of structural and functional bone grafts. The BMSC sheets were harvested from culture flasks and wrapped around pre-seeded composite scaffolds. The layered cell sheets integrated well with the scaffold/cell construct and remained viable, with mineralized nodules visible both inside and outside the scaffold for up to 8 weeks culture. Cells within the constructs underwent classical in vitro osteogenic differentiation with the associated elevation of alkaline phosphatase activity and bone-related protein expression. In vivo, two sets of cell-sheet-scaffold/cell constructs were transplanted under the skin of nude rats. The first set of constructs (554mm3) were assembled with BMSC sheets and cultured for 8 weeks before implantation. The second set of constructs (10104mm3) was implanted immediately after assembly with BMSC sheets, with no further in vitro culture. For both groups, neo cortical and well-vascularised cancellous bone were formed within the constructs with up to 40% bone volume. Histological and immunohistochemical examination revealed that neo bone tissue formed from the pool of seeded BMSC and the bone formation followed predominantly an endochondral pathway, with woven bone matrix subsequently maturing into fully mineralized compact bone; exhibiting the histological markers of native bone. These findings demonstrate that large bone tissues similar to native bone can be regenerated utilizing BMSC sheet techniques in conjunction with composite scaffolds whose structures are optimized from a mechanical, nutrient transport and vascularization perspective.

Low temperature synthesis of zeolite N from kaolinites and montmorillonites

Zeolite N was produced from a variety of kaolinites and montmorillonites at low temperature (b100 °C) in a constantly stirred reactor using potassic and potassic+sodic mother liquors with chloride or hydroxyl anions. Reactions were complete (N95% product) in less than 20 h depending on initial batch composition and type of clay minerals. Zeolite N with 1.0bSi/Alb2.2 was produced under these conditions using KOH in the presence of KCl, NaCl, KCl+NaCl and KCl+NaOH. Zeolite N was also formed under high potassium molarities in the absence of KCl. Zeolite synthesis was more sensitive to water content and temperature when sodium was used in initial batch compositions. Syntheses of zeolite N by these methods were undertaken at bench, pilot and industrial scales.

Exploring the needs for SMEs for mobile data technologies : the role of qualitative research techniques

The seemingly exponential nature of technological change provides SMEs with a complex and challenging operational context. The development of infrastructures capable of supporting the wireless application protocol (WAP) and associated 'wireless' applications represents the latest generation of technological innovation with potential appeals to SMEs and end-users alike. This paper aims to understand the mobile data technology needs of SMEs in a regional setting. The research was especially concerned with perceived needs across three market segments : non-adopters, partial-adopters and full-adopters of new technology. The research was exploratory in nature as the phenomenon under scrutiny is relatively new and the uses unclear, thus focus groups were conducted with each of the segments. The paper provides insights for business, industry and academics.

What measure of temperature is the best predictor of mortality?

Hot and cold temperatures significantly increase mortality rates around the world, but which measure of temperature is the best predictor of mortality is not known. We used mortality data from 107 US cities for the years 1987–2000 and examined the association between temperature and mortality using Poisson regression and modelled a non-linear temperature effect and a non-linear lag structure. We examined mean, minimum and maximum temperature with and without humidity, and apparent temperature and the Humidex. The best measure was defined as that with the minimum cross-validated residual. We found large differences in the best temperature measure between age groups, seasons and cities, and there was no one temperature measure that was superior to the others. The strong correlation between different measures of temperature means that, on average, they have the same predictive ability. The best temperature measure for new studies can be chosen based on practical concerns, such as choosing the measure with the least amount of missing data.

Lateral-torsional buckling of cold-formed steel beams at ambient temperature

Cold-formed steel beams are increasingly used as floor joists and bearers in residential, industrial and commercial buildings. Their structural behaviour and moment capacities are influenced by lateral-torsional buckling and hence a research study was undertaken to investigate the lateral-torsional buckling behaviour of cold-formed steel lipped channel beams at ambient and elevated temperatures. For this purpose a finite element model of a simply supported cold-formed steel lipped channel beam under uniform bending was developed first and validated using available numberical and experimental results. It was then used in a detailed parametric study to simulate the lateral-torsional behaviour of cold-formed steel beams under varying conditions. The moment capacity results were then compared with the predictions from the current ambient temperature design rules in Australia, New Zealand, American and European codes for cold-formed steel structures. Some very interesting results have been obtained. European design rules are found to be conservative while Australian and American design rules are unsafe. This paper presents the results of finite element analyses for ambient temperature conditions, and the comparison with the current design rules.

Evaluation of piezoelectric poly(vinylidene fluoride) polymers for use in space environments. I. Temperature limitations

Smart materials, such as thin-film piezoelectric polymers, are interesting for potential applications on Gossamer spacecraft. This investigation aims to predict the performance and long-term stability of the piezoelectric properties of poly(vinylidene fluoride) (PVDF) and its copolymers under conditions simulating the low-Earthorbit environment. To examine the effects of temperature on the piezoelectric properties of PVDF, poly(vinylidenefluoride-co-trifluoroethylene), and poly(vinylidenefluoride-cohexafluoropropylene), the d33 piezoelectric coefficients were measured up to 160 8C, and the electric displacement/electric field (D–E) hysteresis loops were measured from �80 to þ110 8C. The room-temperature d33 coefficient of PVDF homopolymer films, annealed at 50, 80, and 125 8C, dropped rapidly within a few days of thermal exposure and then remained unchanged. In contrast, the TrFE copolymer exhibited greater thermal stability than the homopolymer, with d33 remaining almost unchanged up to 125 8C. The HFP copolymer exhibited poor retention of d33 at temperatures above 80 8C. In situ D–E loop measurements from �80 to þ110 8C showed that the remanent polarization of the TrFE copolymer was more stable than that of the PVDF homopolymer. D–E hysteresis loop and d33 results were also compared with the deflection of the PVDF homopolymer and TrFE copolymer bimorphs tested over a wide temperature range.

Studies of the radiation chemistry and grafting of a fluoropolymer

The radiation chemistry and the grafting of a fluoropolymer, poly(tetrafluoroethylene-coperfluoropropyl vinyl ether) (PFA), was investigated with the aim of developing a highly stable grafted support for use in solid phase organic chemistry (SPOC). A radiation-induced grafting method was used whereby the PFA was exposed to ionizing radiation to form free radicals capable of initiating graft copolymerization of styrene. To fully investigate this process, both the radiation chemistry of PFA and the grafting of styrene to PFA were examined. Radiation alone was found to have a detrimental effect on PFA when irradiated at 303 K. This was evident from the loss in the mechanical properties due to chain scission reactions. This meant that when radiation was used for the grafting reactions, the total radiation dose needed to be kept as low as possible. The radicals produced when PFA was exposed to radiation were examined using electron spin resonance spectroscopy. Both main-chain (–CF2–C.F–CF2-) and end-chain (–CF2–C.F2) radicals were identified. The stability of the majority of the main-chain radicals when the polymer was heated above the glass transition temperature suggested that they were present mainly in the crystalline regions of the polymer, while the end-chain radicals were predominately located in the amorphous regions. The radical yield at 77 K was lower than the radical yield at 303 K suggesting that cage recombination at low temperatures inhibited free radicals from stabilizing. High-speed MAS 19F NMR was used to identify the non-volatile products after irradiation of PFA over a wide temperature range. The major products observed over the irradiation temperature 303 to 633 K included new saturated chain ends, short fluoromethyl side chains in both the amorphous and crystalline regions, and long branch points. The proportion of the radiolytic products shifted from mainly chain scission products at low irradiation temperatures to extensive branching at higher irradiation temperatures. Calculations of G values revealed that net crosslinking only occurred when PFA was irradiated in the melt. Minor products after irradiation at elevated temperatures included internal and terminal double bonds and CF3 groups adjacent to double bonds. The volatile products after irradiation at 303 K included tetrafluoromethane (CF4) and oxygen-containing species from loss of the perfluoropropyl ether side chains of PFA as identified by mass spectrometry and FTIR spectroscopy. The chemical changes induced by radiation exposure were accompanied by changes in the thermal properties of the polymer. Changes in the crystallinity and thermal stability of PFA after irradiation were examined using DSC and TGA techniques. The equilibrium melting temperature of untreated PFA was 599 K as determined using a method of extrapolation of the melting temperatures of imperfectly formed crystals. After low temperature irradiation, radiation- induced crystallization was prevalent due to scission of strained tie molecules, loss of perfluoropropyl ether side chains, and lowering of the molecular weight which promoted chain alignment and hence higher crystallinity. After irradiation at high temperatures, the presence of short and long branches hindered crystallization, lowering the overall crystallinity. The thermal stability of the PFA decreased with increasing radiation dose and temperature due to the introduction of defect groups. Styrene was graft copolymerized to PFA using -radiation as the initiation source with the aim of preparing a graft copolymer suitable as a support for SPOC. Various grafting conditions were studied, such as the total dose, dose rate, solvent effects and addition of nitroxides to create “living” graft chains. The effect of dose rate was examined when grafting styrene vapour to PFA using the simultaneous grafting method. The initial rate of grafting was found to be independent of the dose rate which implied that the reaction was diffusion controlled. When the styrene was dissolved in various solvents for the grafting reaction, the graft yield was strongly dependent of the type and concentration of the solvent used. The greatest graft yield was observed when the solvent swelled the grafted layers and the substrate. Microprobe Raman spectroscopy was used to map the penetration of the graft into the substrate. The grafted layer was found to contain both poly(styrene) (PS) and PFA and became thicker with increasing radiation dose and graft yield which showed that grafting began at the surface and progressively penetrated the substrate as the grafted layer was swollen. The molecular weight of the grafted PS was estimated by measuring the molecular weight of the non-covalently bonded homopolymer formed in the grafted layers using SEC. The molecular weight of the occluded homopolymer was an order of magnitude greater than the free homopolymer formed in the surrounding solution suggesting that the high viscosity in the grafted regions led to long PS grafts. When a nitroxide mediated free radical polymerization was used, grafting occurred within the substrate and not on the surface due to diffusion of styrene into the substrate at the high temperatures needed for the reaction to proceed. Loading tests were used to measure the capacity of the PS graft to be functionialized with aminomethyl groups then further derivatized. These loading tests showed that samples grafted in a solution of styrene and methanol had superior loading capacity over samples graft using other solvents due to the shallow penetration and hence better accessibility of the graft when methanol was used as a solvent.

Temperature and electric field dependent mobility in poly„3-hexylthiophene diodes

Current-voltage (I-V) curves of Poly(3-hexyl-thiophene) (P3HT) diodes have been collected to investigate the polymer hole-dominated charge transport. At room temperature and at low electric fields the I-V characteristic is purely Ohmic whereas at medium-high electric fields, experimental data shows that the hole transport is Trap Dominated - Space Charge Limited Current (TD-SCLC). In this regime, it is possible to extract the I-V characteristic of the P3HT/Al junction showing the ideal Schottky diode behaviour over five orders of magnitude. At high-applied electric fields, holes’ transport is found to be in the trap free SCLC regime. We have measured and modelled in this regime the holes’ mobility to evaluate its dependence from the electric field applied and the temperature of the device.

Fixing the volatile : studio vocal performance techniques

The process of compiling a studio vocal performance from many takes can often result in the performer producing a new complete performance once this new "best of" assemblage is heard back. This paper investigates the ways that the physical process of recording can alter vocal performance techniques, and in particular, the establishing of a definitive melodic and rhythmic structure. Drawing on his many years of experience as a commercially successful producer, including the attainment of a Grammy award, the author will analyse the process of producing a “credible” vocal performance in depth, with specific case studies and examples. The question of authenticity in rock and pop will also be discussed and, in this context, the uniqueness of the producer’s role as critical arbiter – what gives the producer the authority to make such performance evaluations? Techniques for creating conditions in the studio that are conducive to vocal performances, in many ways a very unnatural performance environment, will be discussed, touching on areas such as the psycho-acoustic properties of headphone mixes, the avoidance of intimidatory practices, and a methodology for inducing the perception of a “familiar” acoustic environment.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.