Fuzzy reasoning in confidence evaluation of speech recognition

Confidence measures represent a systematic way to express reliability of speech recognition results. A common approach to confidence measuring is to take profit of the information that several recognition-related features offer and to combine them, through a given compilation mechanism , into a more effective way to distinguish between correct and incorrect recognition results. We propose to use a fuzzy reasoning scheme to perform the information compilation step. Our approach opposes the previously proposed ones because ours treats the uncertainty of recognition hypotheses in terms ofPeer ReviewedPostprint (published version

Contextual confidence measures for continuous speech recognition

This paper explores the repercussion of contextual information into confidence measuring for continuous speech recognition results. Our approach comprises three steps: to extract confidence predictors out of recognition results, to compile those predictors into confidence measures by means of a fuzzy inference system whose parameters have been estimated, directly from examples, with an evolutionary strategy and, finally, to upgrade the confidence measures by the inclusion of contextual information. Through experimentation with two different continuous speech application tasks, results show that the context re-scoring procedure improves the capabilities of confidence measures to discriminate between correct and incorrect recognition results for every level of thresholding, even when a rather simple method to add contextual information is considered.Peer ReviewedPostprint (published version

Robotic additive manufacturing system featuring wire deposition by electric arc for high-value manufacturing

Increasing demand from the high-value manufacturing industries of quality, productivity, efficiency and security aligns with the ambition and driving need for novel automated robotic systems. This paper describes the motivation, design and implementation phases of the SERFOW project (Smart Enabling Robotics driving Free Form Welding). SERFOW is an automated additive manufacturing arc and wire tungsten inert gas (TIG) welding prototype to support industrial manufacturing requirements of the nuclear, aerospace and automotive industry sectors. Key innovations are found in the integration of a 3D vision system with a robotic manipulator to perform automatic free-form fusion welding for the multiple layer additive material build-up required to expand Additive Manufacturing (AM) with minimum human intervention. Welding trials were performed on samples made of Super Duplex stainless steel alloy. Metallographic observations were performed to analyze the porosity distribution and penetration on the material after welding. Also, temperature, feritescope and tensile measurements were performed. The results showed that the welding and AM process performed with the SERFOW cell are within an acceptable quality tolerance range according to the ISO 5817 and the ASME A789 welding standards

Fuzzy reasoning in confidence evaluation of speech recognition

Confidence measures represent a systematic way to express reliability of speech recognition results. A common approach to confidence measuring is to take profit of the information that several recognition-related features offer and to combine them, through a given compilation mechanism , into a more effective way to distinguish between correct and incorrect recognition results. We propose to use a fuzzy reasoning scheme to perform the information compilation step. Our approach opposes the previously proposed ones because ours treats the uncertainty of recognition hypotheses in terms ofPeer Reviewe

Contextual confidence measures for continuous speech recognition

This paper explores the repercussion of contextual information into confidence measuring for continuous speech recognition results. Our approach comprises three steps: to extract confidence predictors out of recognition results, to compile those predictors into confidence measures by means of a fuzzy inference system whose parameters have been estimated, directly from examples, with an evolutionary strategy and, finally, to upgrade the confidence measures by the inclusion of contextual information. Through experimentation with two different continuous speech application tasks, results show that the context re-scoring procedure improves the capabilities of confidence measures to discriminate between correct and incorrect recognition results for every level of thresholding, even when a rather simple method to add contextual information is considered.Peer Reviewe

Proof of concept for a virtual reality environment used for intervention planning and training in highly radioactive environments

This paper presents a novel way to predict radiation dose using immersive Virtual Reality (VR). The platform allows an assessment of proposed interventions in as much detail and time as required. Its purpose is to give users the maximum amount of agency while in the environment. Workers get a realistic experience practising jobs and supervisors can oversee the expected radiation doses for each intervention. A proof of concept performed and showed the platform returned a comparable result to the real radiation exposure for a predefined route. The errors of the system are dependant on the dose map. With an accurate dose map, the system will produce reliable results

The urgent need for robust coral disease diagnostics

Coral disease has emerged over recent decades as a significant threat to coral reef ecosystems, with declines in coral cover and diversity of Caribbean reefs providing an example of the potential impacts of disease at regional scales. If similar trends are to be mitigated or avoided on reefs worldwide, a deeper understanding of the factors underlying the origin and spread of coral diseases and the steps that can be taken to prevent, control, or reduce their impacts is required. In recent years, an increased focus on coral microbiology and the application of classic culture techniques and emerging molecular technologies has revealed several coral pathogens that could serve as targets for novel coral disease diagnostic tools. The ability to detect and quantify microbial agents identified as indicators of coral disease will aid in the elucidation of disease causation and facilitate coral disease detection and diagnosis, pathogen monitoring in individuals and ecosystems, and identification of pathogen sources, vectors, and reservoirs. This information will advance the field of coral disease research and contribute knowledge necessary for effective coral reef management. This paper establishes the need for sensitive and specific molecular-based coral pathogen detection, outlines the emerging technologies that could serve as the basis of a new generation of coral disease diagnostic assays, and addresses the unique challenges inherent to the application of these techniques to environmentally derived coral samples