Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

In Response: "Oral versus IV treatment for catheter-related bloodstream infections - by Burke A. Cunha"

In his letter Cunha suggests that oral antibiotic therapy is safer and less expensive than intravenous therapy via central venous catheters (CVCs) (1). The implication is that costs will fall and increased health benefits will be enjoyed resulting in a gain in efficiency within the healthcare system. CVCs are often used in critically ill patients to deliver antimicrobial therapy, but expose patients to a risk of catheter-related bloodstream infection (CRBSI). Our current knowledge about the efficiency (i.e. costeffectiveness) of allocating resources toward interventions that prevent CRBSI in patients requiring a CVC has already been reviewed (2). If for some patient groups antimicrobial therapy can be delivered orally, instead of through a CVC, then the costs and benefits of this alternate strategy should be evaluated...

Likelihood-maximising frameworks for enhanced in-car speech recognition

Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating non-critical in-car systems. Likelihood-maximising (LIMA) frameworks optimise speech enhancement algorithms based on recognised state sequences rather than traditional signal-level criteria such as maximising signal-to-noise ratio. Previously presented LIMA frameworks require calibration utterances to generate optimised enhancement parameters which are used for all subsequent utterances. Sub-optimal recognition performance occurs in noise conditions which are significantly different from that present during the calibration session - a serious problem in rapidly changing noise environments. We propose a dialog-based design which allows regular optimisation iterations in order to track the changing noise conditions. Experiments using Mel-filterbank spectral subtraction are performed to determine the optimisation requirements for vehicular environments and show that minimal optimisation assists real-time operation with improved speech recognition accuracy. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session.

Assessment of Speech Dialog Systems using Multi-Modal Cognitive Load Analysis and Driving Performance Metrics

In this paper, cognitive load analysis via acoustic- and CAN-Bus-based driver performance metrics is employed to assess two different commercial speech dialog systems (SDS) during in-vehicle use. Several metrics are proposed to measure increases in stress, distraction and cognitive load and we compare these measures with statistical analysis of the speech recognition component of each SDS. It is found that care must be taken when designing an SDS as it may increase cognitive load which can be observed through increased speech response delay (SRD), changes in speech production due to negative emotion towards the SDS, and decreased driving performance on lateral control tasks. From this study, guidelines are presented for designing systems which are to be used in vehicular environments.

Creative Commons : fair to share?

This article explores new the realities of the permissions culture and “all rights reserved copyright” in the networked environment and poses the question: why is lending a copy of a book sharing but emailing a PDF of it piracy? It explores new approaches to publishing and distribution of books by highlighting two books in the Aduki Independent Press catalogue. It was modeled on a presentation delivered by Elliott Bledsoe at the Changing Climates in Arts Publishing forum run by Artlink and the Copyright Agency Limited in Adelaide, Australia on 9 May 2009 and in Sydney, Australia on 27 June 2009.

FPGA implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement and recognition

In an automotive environment, the performance of a speech recognition system is affected by environmental noise if the speech signal is acquired directly from a microphone. Speech enhancement techniques are therefore necessary to improve the speech recognition performance. In this paper, a field-programmable gate array (FPGA) implementation of dual-microphone delay-and-sum beamforming (DASB) for speech enhancement is presented. As the first step towards a cost-effective solution, the implementation described in this paper uses a relatively high-end FPGA device to facilitate the verification of various design strategies and parameters. Experimental results show that the proposed design can produce output waveforms close to those generated by a theoretical (floating-point) model with modest usage of FPGA resources. Speech recognition experiments are also conducted on enhanced in-car speech waveforms produced by the FPGA in order to compare recognition performance with the floating-point representation running on a PC.

Impact of cognitive load and frustration on drivers’ speech [Abstract]

Secondary tasks such as cell phone calls or interaction with automated speech dialog systems (SDSs) increase the driver’s cognitive load as well as the probability of driving errors. This study analyzes speech production variations due to cognitive load and emotional state of drivers in real driving conditions. Speech samples were acquired from 24 female and 17 male subjects (approximately 8.5 h of data) while talking to a co-driver and communicating with two automated call centers, with emotional states (neutral, negative) and the number of necessary SDS query repetitions also labeled. A consistent shift in a number of speech production parameters (pitch, first format center frequency, spectral center of gravity, spectral energy spread, and duration of voiced segments) was observed when comparing SDS interaction against co-driver interaction; further increases were observed when considering negative emotion segments and the number of requested SDS query repetitions. A mel frequency cepstral coefficient based Gaussian mixture classifier trained on 10 male and 10 female sessions provided 91% accuracy in the open test set task of distinguishing co-driver interactions from SDS interactions, suggesting—together with the acoustic analysis—that it is possible to monitor the level of driver distraction directly from their speech.

Robust speech recognition using speech enhancement

Automatic Speech Recognition (ASR) has matured into a technology which is becoming more common in our everyday lives, and is emerging as a necessity to minimise driver distraction when operating in-car systems such as navigation and infotainment. In “noise-free” environments, word recognition performance of these systems has been shown to approach 100%, however this performance degrades rapidly as the level of background noise is increased. Speech enhancement is a popular method for making ASR systems more ro- bust. Single-channel spectral subtraction was originally designed to improve hu- man speech intelligibility and many attempts have been made to optimise this algorithm in terms of signal-based metrics such as maximised Signal-to-Noise Ratio (SNR) or minimised speech distortion. Such metrics are used to assess en- hancement performance for intelligibility not speech recognition, therefore mak- ing them sub-optimal ASR applications. This research investigates two methods for closely coupling subtractive-type enhancement algorithms with ASR: (a) a computationally-efficient Mel-filterbank noise subtraction technique based on likelihood-maximisation (LIMA), and (b) in- troducing phase spectrum information to enable spectral subtraction in the com- plex frequency domain. Likelihood-maximisation uses gradient-descent to optimise parameters of the enhancement algorithm to best fit the acoustic speech model given a word se- quence known a priori. Whilst this technique is shown to improve the ASR word accuracy performance, it is also identified to be particularly sensitive to non-noise mismatches between the training and testing data. Phase information has long been ignored in spectral subtraction as it is deemed to have little effect on human intelligibility. In this work it is shown that phase information is important in obtaining highly accurate estimates of clean speech magnitudes which are typically used in ASR feature extraction. Phase Estimation via Delay Projection is proposed based on the stationarity of sinusoidal signals, and demonstrates the potential to produce improvements in ASR word accuracy in a wide range of SNR. Throughout the dissertation, consideration is given to practical implemen- tation in vehicular environments which resulted in two novel contributions – a LIMA framework which takes advantage of the grounding procedure common to speech dialogue systems, and a resource-saving formulation of frequency-domain spectral subtraction for realisation in field-programmable gate array hardware. The techniques proposed in this dissertation were evaluated using the Aus- tralian English In-Car Speech Corpus which was collected as part of this work. This database is the first of its kind within Australia and captures real in-car speech of 50 native Australian speakers in seven driving conditions common to Australian environments.

Venture creation and resource processes : using Bricolage sustainability ventures

Principal Topic: Resource decisions are critical to the venture creation process, which has important subsequent impacts on venture creation and performance (Boeker, 1989). Most entrepreneurs however, suffer substantial resource constraints in venture creation and during venture growth (Shepherd et al., 2000). Little is known about how high potential, sustainability ventures (the ventures of interest in this research), despite resource constraints, achieve continued venture persistence and venture success. One promising theory that explicitly links to resource constraints is a concept developed by Levi Strauss (1967) termed bricolage. Bricolage aligns with notions of resourcefulness: using what's on hand, through making do, and recombining resources for new or novel purposes (Baker & Nelson 2005). To the best of our knowledge, previous studies have not systematically investigated internal and external constraints, their combinations, and subsequent bricolage patterns. The majority of bricolage literature focuses on external environmental constraints (e.g. Wieck 1989; Baker & Nelson 2005), thereby paying less attention to in evaluating internal constraints (e.g. skills and capabilities) or constraint combinations. In this paper we focus on ventures that typically face resource-poor environments. High potential, nascent and young sustainability ventures are often created and developed with resource constraints and in some cases, have greater resource requirements owing to higher levels of technical sophistication of their products (Rothaermel & Deeds 2006). These ventures usually have high aspirations and potential for growth who ''seeks to meet the needs and aspirations without compromising the ability to meet those of the future'' (Brundtland Commission 1983). High potential ventures are increasingly attributed with a central role in the development of innovation, and employment in developed economies (Acs 2008). Further, increasing awareness of environmental and sustainability issues has fostered demand for business processes that reduce detrimental environmental impacts of global development (Dean & McMullen 2007) and more environmentally sensitive products and services: representing an opportunity for the development of ventures that seek to satisfy this demand through entrepreneurial action. These ventures may choose to ''make do'' with existing resources in developing resource combinations that produce the least impact on the environment. The continuous conflict between the greater requirements for resources and limited resource availability in high potential sustainable ventures, with the added complexity of balancing this with an uncompromising focus on using ''what's on hand'' to lessen environment impacts may make bricolage behaviours critical for these ventures. Research into bricolage behaviour is however, the exception rather than the rule (Cunha 2005). More research is therefore needed to further develop and extend this emerging concept, especially in the context of sustainability ventures who are committed to personal and social goals of resourcefulness. To date, however, bricolage has not been studied specifically among high potential sustainable ventures. This research seeks to develop an in depth understanding of the impact of internal and external constraints and their combinations on the mechanisms employed in bricolage behaviours in differing dynamic environments. The following research question was developed to investigate this: How do internal, external resource constraints (or their combinations) impact bricolage resource decisions in high potential sustainability ventures? ---------- Methodology/Key Propositions: 6 case studies will be developed utilizing survey data from the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) large-scale longitudinal study of new venture start-ups in Australia. Prior to commencing case studies, 6 scoping interviews were conducted with key stakeholders including industry members, established businesses and government to ensure practical relevance in case development. The venture is considered the unit of analysis with the key informant being the entrepreneur and other management team members where appropriate. Triangulation techniques are used in this research including semi-structured interviews, survey data, onsite visits and secondary documentation website analysis, resumes, and business plans. These 6 sustainability ventures have been selected based on different environmental dynamism conditions including a traditionally mature market (building industry) and a more dynamic, evolving industry (renewable energy/solar ventures). In evaluating multidisciplinary literature, we expect the following external constraints are critical including: technology constraints (seen through lock-in of incumbents existing technology), institutional regulation and standards, access to markets, knowledge and training to nascent and young venture bricolage processes. The case studies will investigate internal constraints including resource fungability, resource combination capabilities, translating complex science/engineering knowledge into salient, valuable market propositions, i.e. appropriate market outcomes, and leveraging relationships may further influence bricolage decisions. ---------- Results and Implications: Intended ventures have been identified within the CAUSEE sample and have agreed to participate and secondary data collection for triangulation purposes has already commenced. Data collection of the case studies commenced 27th of May 2009. Analysis is expected to be completed finalised by 25th September 2009. This paper will report on the pattern of resource constraints and its impact on bricolage behaviours: its subsequent impact on resource deployment within venture creation and venture growth. As such, this research extends the theory of bricolage through the systematic analysis of constraints on resource management processes in sustainability ventures. For practice, this research may assist in providing a better understanding of the resource requirements and processes needed for continued venture persistence and growth in sustainability ventures. In these times of economic uncertainty, a better understanding of the influence on constraints and bricolage: the interplay of behaviours, processes and outcomes may enable greater venture continuance and success.

A hydrogen tethered inhibitor of matrix metalloproteinases

During wound repair, the balance between matrix metalloproteinases (MMPs) and their natural inhibitors (the TIMPs) is crucial for the normal extra cellular matrix turnover. However, the over expression of several MMPs including MMP-1, 2, 3, 8, 9 and MMP-10, combined with abnormally high levels of activation or low expression of TIMPs, may contribute to excessive degradation of connective tissue and formation of chronic ulcers. There are many groups exploring strategies for promoting wound healing involving delivery of growth factors, cells, ECM components and small molecules. Our approach for improving the balance of MMPs is not to add anything more to the wound, but instead to neutralise the over-expressed MMPs using inhibitors tethered to a bandage-like hydrogel. Our in vitro experiments using designed synthetic pseudo peptide inhibitors have been demonstrated to inhibit MMP activity in standard solutions. These inhibitors have also been tethered to polyethylene glycol hydrogels using a facile reaction between the linker unit on the inhibitor and the gel. After tethering the inhibition of MMPs diminishes to some extent and we postulate that this arises due to poor diffusion of the MMPs into the gels. When the tethered inhibitors were tested against chronic wound fluid obtained against patients we observed over 40% inhibition in proteolytic activity suggesting our approach may prove useful in rebalancing MMPs within chronic wounds.

Analysis and detection of cognitive load and frustration in drivers' speech

Non-driving related cognitive load and variations of emotional state may impact a driver’s capability to control a vehicle and introduces driving errors. Availability of reliable cognitive load and emotion detection in drivers would benefit the design of active safety systems and other intelligent in-vehicle interfaces. In this study, speech produced by 68 subjects while driving in urban areas is analyzed. A particular focus is on speech production differences in two secondary cognitive tasks, interactions with a co-driver and calls to automated spoken dialog systems (SDS), and two emotional states during the SDS interactions - neutral/negative. A number of speech parameters are found to vary across the cognitive/emotion classes. Suitability of selected cepstral- and production-based features for automatic cognitive task/emotion classification is investigated. A fusion of GMM/SVM classifiers yields an accuracy of 94.3% in cognitive task and 81.3% in emotion classification.

A peptidomimetic inhibitor of matrix metalloproteinases containing a tetherable linker group

Successful wound repair and normal turnover of the extracellular matrix relies on a balance between matrix metalloproteinases (MMPs) and their natural inhibitors (the TIMPs). When over-expression of MMPs and abnormally high levels of activation or low expression of TIMPs are encountered, excessive degradation of connective tissue and the formation of chronic ulcers can occur. One strategy to rebalance MMPs and TIMPs is to use inhibitors. We have designed a synthetic pseudopeptide inhibitor with an amine linker group based on a known high-affinity peptidomimetic MMP inhibitor have demonstrated inhibition of MMP-1, -2, -3 and -9 activity in standard solutions. The inhibitor was also tethered to a polyethylene glycol hydrogel using a facile reaction between the linker unit on the inhibitor and the hydrogel precursors. After tethering, we observed inhibition of the MMPs although there was an increase in the IC50s which was attributed to poor diffusion of the MMPs into the hydrogels, reduced activity of the tethered inhibitor or incomplete incorporation of the inhibitor into the hydrogels. When the tethered inhibitors were tested against chronic wound fluid we observed significant inhibition in proteolytic activity suggesting our approach may prove useful in rebalancing MMPs within chronic wounds.

The effect of dialect mismatch on likelihood-maximising speech enhancement for noise-robust speech recognition

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but these approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks are an alternative that optimise parameters of enhancement algorithms based on state sequences generated for utterances with known transcriptions. Previous reports of LIMA frameworks have shown significant promise for improving speech recognition accuracies under additive background noise for a range of speech enhancement techniques. In this paper we discuss the drawbacks of the LIMA approach when multiple layers of acoustic mismatch are present – namely background noise and speaker accent. Experimentation using LIMA-based Mel-filterbank noise subtraction on American and Australian English in-car speech databases supports this discussion, demonstrating that inferior speech recognition performance occurs when a second layer of mismatch is seen during evaluation.

The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.