A Likelihood-Maximizing Framework for Enhanced In-Car Speech Recognition Based on Speech Dialog System Interaction

Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating noncritical in-car systems. Under such conditions, however, speech recognition accuracy degrades significantly, and techniques such as speech enhancement are required to improve these accuracies. Likelihood-maximizing (LIMA) frameworks optimize speech enhancement algorithms based on recognized state sequences rather than traditional signal-level criteria such as maximizing signal-to-noise ratio. LIMA frameworks typically require calibration utterances to generate optimized enhancement parameters that are used for all subsequent utterances. Under such a scheme, suboptimal recognition performance occurs in noise conditions that are significantly different from that present during the calibration session – a serious problem in rapidly changing noise environments out on the open road. In this chapter, we propose a dialog-based design that allows regular optimization iterations in order to track the ever-changing noise conditions. Experiments using Mel-filterbank noise subtraction (MFNS) are performed to determine the optimization requirements for vehicular environments and show that minimal optimization is required to improve speech recognition, avoid over-optimization, and ultimately assist with semireal-time operation. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session only.

Functional MRI of facial emotion recognition deficits in schizophrenia and their electrophysiological correlates

Empirical evidence suggests impaired facial emotion recognition in schizophrenia. However, the nature of this deficit is the subject of ongoing research. The current study tested the hypothesis that a generalized deficit at an early stage of face-specific processing (i.e. putatively subserved by the fusiform gyrus) accounts for impaired facial emotion recognition in schizophrenia as opposed to the Negative Emotion-specific Deficit Model, which suggests impaired facial information processing at subsequent stages. Event-related potentials (ERPs) were recorded from 11 schizophrenia patients and 15 matched controls while performing a gender discrimination and a facial emotion recognition task. Significant reduction of the face-specific vertex positive potential (VPP) at a peak latency of 165 ms was confirmed in schizophrenia subjects whereas their early visual processing, as indexed by P1, was found to be intact. Attenuated VPP was found to correlate with subsequent P3 amplitude reduction and to predict accuracy when performing a facial emotion discrimination task. A subset of ten schizophrenia patients and ten matched healthy control subjects also performed similar tasks in the magnetic resonance imaging scanner. Patients showed reduced blood oxygenation level-dependent (BOLD) activation in the fusiform, inferior frontal, middle temporal and middle occipital gyrus as well as in the amygdala. Correlation analyses revealed that VPP and the subsequent P3a ERP components predict fusiform gyrus BOLD activation. These results suggest that problems in facial affect recognition in schizophrenia may represent flow-on effects of a generalized deficit in early visual processing.

A generalised deficit can account for problems in facial emotion recognition in schizophrenia

Neuroimaging research has shown localised brain activation to different facial expressions. This, along with the finding that schizophrenia patients perform poorly in their recognition of negative emotions, has raised the suggestion that patients display an emotion specific impairment. We propose that this asymmetry in performance reflects task difficulty gradations, rather than aberrant processing in neural pathways subserving recognition of specific emotions. A neural network model is presented, which classifies facial expressions on the basis of measurements derived from human faces. After training, the network showed an accuracy pattern closely resembling that of healthy subjects. Lesioning of the network led to an overall decrease in the network’s discriminant capacity, with the greatest accuracy decrease to fear, disgust and anger stimuli. This implies that the differential pattern of impairment in schizophrenia patients can be explained without having to postulate impairment of specific processing modules for negative emotion recognition.

Automated counting of the Northern Pacific Sea Star in the Derwent using shape recognition

A novel shape recognition algorithm was developed to autonomously classify the Northern Pacific Sea Star (Asterias amurenis) from benthic images that were collected by the Starbug AUV during 6km of transects in the Derwent estuary. Despite the effects of scattering, attenuation, soft focus and motion blur within the underwater images, an optimal joint classification rate of 77.5% and misclassification rate of 13.5% was achieved. The performance of algorithm was largely attributed to its ability to recognise locally deformed sea star shapes that were created during the segmentation of the distorted images.

Criminal Justice Ethics : Cultivating the Moral Imagination

It is essential for those employed within the justice system to be able to competently and confidently work at the borders between ethics and the law. Criminal Justice Ethics offers a fresh new approach to considering ethical issues in a criminal justice context. Rather than simply offering a range of ethical dilemmas specific to various justice professionals, it provides extensive discussion of how individuals develop their 'moral imaginations' using ethical perspectives and practices, both as citizens of the world and as practitioners of justice. Starting from a consideration of the major ethical theories, this book sets the framework for an expansive discussion of ethics by moving from theory to consider the just society and the role of the justice professional within it. Each chapter provides detailed analysis of relevant ethical issues, and activities to engage students with the content, as well as review questions, which can be used for revision or examination. This book will help students to: • understand the various theoretical approaches to ethics, • apply these understandings to issues in society and the justice process, • assist in developing the ability to investigate, discuss, and analyse current ethical issues in criminal justice, • appreciate the diverse nature of ethical systems across cultures, • outline strategies for detecting and resolving ethical dilemmas. Rich with examples and ethical dilemmas from a broad range of contexts, this book's multicultural approach will appeal not only to criminal justice educators, but also to academics, students and practitioners approaching criminal justice from sociological, psychological or philosophical perspectives.

Localization and place recognition using an ultra-wide band (UWB) radar

This paper presents an approach to mobile robot localization, place recognition and loop closure using a monostatic ultra-wide band (UWB) radar system. The UWB radar is a time-of-flight based range measurement sensor that transmits short pulses and receives reflected waves from objects in the environment. The main idea of the poposed localization method is to treat the received waveform as a signature of place. The resulting echo waveform is very complex and highly depends on the position of the sensor with respect to surrounding objects. On the other hand, the sensor receives similar waveforms from the same positions.Moreover, the directional characteristics of dipole antenna is almost omnidirectional. Therefore, we can localize the sensor position to find similar waveform from waveform database. This paper proposes a place recognitionmethod based on waveform matching, presents a number of experiments that illustrate the high positon estimation accuracy of our UWB radar-based localization system, and shows the resulting loop detection performance in a typical indoor office environment and a forest.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Ubuntu and business ethics: Problems, perspectives and prospects

The African philosophy of Ubuntu is typically characterised as a communitarian philosophy that emphasises virtues such as compassion, tolerance and harmony. In recent years there has been growing interest in this philosophy, and in how it can be applied to a variety of disciplines and issues. Several authors have provided useful introductions of Ubuntu in the field of business ethics and suggested theoretical ways in which it could be applied. The purpose of this paper is to extend this discussion by providing a more critical analysis of Ubuntu and business ethics with the aim of clarifying the role that Ubuntu can play, and providing guidance for further research in this area. The analysis consists of three sections. In the first, certain problems are identified within the existing literature. This is followed by a consideration of alternative perspectives and interpretations of Ubuntu. The last section, following from the first two, identifies specific areas requiring further research, both empirical and non-empirical, as well as ways in which Ubuntu could be fruitfully applied.

The ethics of corporate governance: A (South)African perspective

Purpose – The purpose of this paper is to review the developments in South African corporate governance since the end of apartheid, with a view to identifying themes and points of convergence and/or divergence with other models. Design/methodology/approach – The paper presents a critical review of South African corporate governance in the context of political and economic developments. Where relevant, aspects of corporate governance theory (in particular the stakeholder and shareholder debate) are considered in the South African context. Findings – South African corporate governance can be seen to broadly follow Anglo‐American examples with the notable exception of the stakeholder approach of the two King reports. This approach emphasises the responsibilities of companies to various stakeholders and encourages stakeholder engagement as an integral element of company strategy. There has not, however, been any substantial incorporation of stakeholder interests into formal corporate governance structures such as board structure and financial reporting. Practical implications – The ongoing consideration of corporate governance developments in South Africa is important for its continued development in the country and the region. Originality/value – A review of South African corporate governance is timely given the probable release of the third King report in 2009, together with new company legislation.

Automatic image scaling for place recognition in changing environments

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.