Evaluation of noisy transcripts for spoken document retrieval

Spoken Document Retrieval (SDR) is usually implemented by using an Information Retrieval (IR) engine on speech transcripts that are produced by an Automatic Speech Recognition (ASR) system. These transcripts generally contain a substantial amount of transcription errors (noise) and are mostly unstructured. This thesis addresses two challenges that arise when doing IR on this type of source material: i. segmentation of speech transcripts into suitable retrieval units, and ii. evaluation of the impact of transcript noise on the results of an IR task.\ud It is shown that intrinsic evaluation results in different conclusions with regard to the quality of automatic story boundaries than when (extrinsic) Mean Average Precision (MAP) is used. This indicates that for automatic story segmentation for search applications, the traditionally used (intrinsic) segmentation cost may not be a good performance target. The best performance in an SDR context was achieved using lexical cohesion-based approaches, rather than the statistical approaches that were most popular in story segmentation benchmarks.\ud For the evaluation of speech transcript noise in an SDR context a novel framework is introduced, in which evaluation is done in an extrinsic, and query-dependent manner but without depending on relevance judgments. This is achieved by making a direct comparison between the ranked results lists of IR tasks on a reference and an ASR-derived transcription. The resulting measures are highly correlated with MAP, making it possible to do extrinsic evaluation of ASR transcripts for ad-hoc collections, while using a similar amount of reference material as the popular intrinsic metric Word Error Rate.\ud The proposed evaluation methods are expected to be helpful for the task of optimizing the configuration of ASR systems for the transcription of (large) speech collections for use in Spoken Document Retrieval, rather than the more traditional dictation tasks

COalitions in COOperation Networks (COCOON):Social Network Analysis and Game Theory to Enhance Cooperation Networks

Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.IdSpace, SIK

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research