Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

The play's the thing? : Hamlet, Slings & Arrows, and the authority of theatrical consumption

This paper reads season 1 of the critically-acclaimed Canadian television series “Slings & Arrows” (2003). This six-episode series is set in a fictionalised version of the Stratford Festival, and tells the story of a plagued production of Shakespeare’s Hamlet. It follows the play’s rehearsal after the death of the festival’s artistic director; Geoffrey Tennant (himself a plagued Hamlet) takes over the role of director, and must face his past in order to produce a Hamlet that will save the festival, redeem his reputation, and repair his interpersonal relationships. Drawing on popular and theatrical understandings of Shakespeare’s play, the series negotiates tropes of metatheatre, filiality, cultural production and consumption, in order to demonstrate the ongoing relevance and legitimacy of “Shakespeare” in the twenty-first century. The “Slings & Arrows” narrative revolves around the doubled-plot of Hamlet and the experiences of the company mounting Hamlet. In quite obvious ways, the show thus thematises ways in which Shakespeare can be used to read one’s own life and world. In the broader sense, however, the show also offers theatre/performance as a catalyst for affect. In doing so, the show functions as a relatively straight adaptation of Hamlet, and a metatheatrical/metafictional commentary on the functions of Hamlet within contemporary culture. In Shakespeare’s play, the production of “The Mouse-Trap” proves, both to Hamlet and the audience, the legitimacy of the ghost’s claims. Similarly, in “Slings & Arrows”, the successful performance of Hamlet legitimises Geoffrey’s position as artistic director of the festival, and affirms for the viewer the value of Shakespearean production in contemporary culture. In each text, theatre/performance enables and legitimises a son carrying out a dead father’s wishes in order to restore or reproduce socio-cultural order. The metatheatrics of these gestures engage the reader/viewer in a self-reflexive process whereby the ‘value’ of theatre is thematised and performed, and the consumer is positioned as the arbiter and agent of that value: complicit in its production even as they are the site of its consumption.

Do Chinese traditional values matter in regulating China’s company directors? Preliminary findings from surveys in Hong Kong

This article argues that Chinese traditional values do matter in Chinese corporate governance. The object is to report on the preliminary findings of a project supported by the General Research Fund in Hong Kong (HK). Thus far the survey results from HK respondents support the authors’ hypothesis. As such, traditional Chinese values should be on the agenda of the next round of company law reforms in China

The first global entertainment company : explaining Pathe's dominance in the pre-Hollywood film industry

For almost a decade before Hollywood existed, French firm Pathe towered over the early film industry with estimates of its share of all films sold around the world varying between 50-70%. Pathe was the first global entertainment company. This paper analyses its rise to market leadership by applying a theoretical framework drawn from the business literature on causes of industry dominance, which provides insights into how firms acquire and maintain market dominance and in this case the film industry. This paper uses evidence presented by film historians to argue that Pathe "fits" the expected theoretical model of a dominant firm because it had a marketing orientation, used an effective quality-based competitive strategy and possessed the six critical marketing capabilities that business research shows enable the best performing firms to consistently outperform rivals.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

Reconnecting the interrupted landscape : a cultural landscape approach to Australian open-cut coal mining landscapes

In the late 20th century, a value-shift began to influence political thinking, recognising the need for environmentally, socially and culturally sustainable resource development. This shift entailed moves away from thinking of nature and culture as separate entities - The former existing merely to serve the latter. Cultural landscape theory recognises 'nature' as at once both 'natural', and as a 'cultural' construct. As such it may offer a framework through which to progress in the quest for 'sustainable development'. This 2005 Masters thesis makes a contribution to that quest by asking whether contemporary developments in cultural landscape theory can contribute to rehabilitation strategies for Australian open-cut coal mining landscapes, an examplar resource development landscape. A thematic historial overview of landscape values and resource development in Australis post-1788, and a review of cultural landscape theory literature contribute to the formation of the theoretical framework: "reconnecting the interrupted landscape". The author then explores a possible application of this framework within the Australian open-cut coal mining landscape.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

Using data mining on road asset management data in analysing road crashes

Road safety is a major concern worldwide. Road safety will improve as road conditions and their effects on crashes are continually investigated. This paper proposes to use the capability of data mining to include the greater set of road variables for all available crashes with skid resistance values across the Queensland state main road network in order to understand the relationships among crash, traffic and road variables. This paper presents a data mining based methodology for the road asset management data to find out the various road properties that contribute unduly to crashes. The models demonstrate high levels of accuracy in predicting crashes in roads when various road properties are included. This paper presents the findings of these models to show the relationships among skid resistance, crashes, crash characteristics and other road characteristics such as seal type, seal age, road type, texture depth, lane count, pavement width, rutting, speed limit, traffic rates intersections, traffic signage and road design and so on.

Road crash proneness prediction using data mining

Developing safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. This paper presents a data mining methodology using decision trees for modeling the crash proneness of road segments using available road and crash attributes. The models quantify the concept of crash proneness and demonstrate that road segments with only a few crashes have more in common with non-crash roads than roads with higher crash counts. This paper also examines ways of dealing with highly unbalanced data sets encountered in the study.

Identifying differences in wet and dry road crashes using data mining

It is commonly accepted that wet roads have higher risk of crash than dry roads; however, providing evidence to support this assumption presents some difficulty. This paper presents a data mining case study in which predictive data mining is applied to model the skid resistance and crash relationship to search for discernable differences in the probability of wet and dry road segments having crashes based on skid resistance. The models identify an increased probability of wet road segments having crashes for mid-range skid resistance values.

Board network characteristics and company performance in Sweden : the case of Gnosjö companies and their board members in southern Sweden

When managers of entrepreneurial companies typically talk about strategies, they first consider what products to make and secondly where to locate the business. The entrepreneurial companies locate in rural areas because of a wish to maintain a certain lifestyle, or because they can combine a resource available there with certain knowledge or interest that they have (Getz and Nilsson, 2004). In addition, many managers of entrepreneurial companies are confident in locating in a rural area, because there often is economic and social structure supportive of local corporate governance. The most central part of corporate governance is the board of directors. In an entrepreneurial company in a rural area, such members of boards are most likely to be individuals in dominant positions influential in the local economy.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.