401 resultados para Metaphor Identification Procedure (MIP)


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Automatic spoken Language Identi¯cation (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identi¯ca- tion systems provide. A prominent application arises in call centers dealing with speakers speaking di®erent languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation. Acoustic and phonetic speech information are targeted as the most suitable fea- tures for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Pho- netic speech information is extracted using existing speech recognition technol- ogy. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by di®erent speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech. As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.

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Aim: Researchers have suggested that approximately 1% of individuals with psychopathic tendencies can successfully function within the community, although there has been a lack of research to support this claim. The current study aimed to identify individuals with psychopathic tendencies within a community sample and furthermore the socio-demographic correlates of this community integrated psychopath (e.g. relationship stability, substance use, and employment status). Procedure: 300 participants completed the Self-Reported Psychopathy scale – version 3 which contains four core psychopathy subfactors: (a) Interpersonal Manipulation, (b) Callous Affect, (c) Erratic Lifestyle and (d) Criminal Tendencies as well as the Paulhus Deception Scales to explore the effect of impression management and self-deception on the identification of psychopathy. Findings: Results indicated that at least 1% of the current community displayed characteristics consistent with psychopathic tendencies. A series of bivariate and multivariate statistical analyses were conducted which indicated that gender, age and alcohol misuse were predictive of psychopathy scores for this sample. More specifically, younger males who tend to misuse alcohol were found to be most likely to have psychopathic tendencies. Interestingly, impression management and self-deception was not associated with such tendencies. Discussion: The results provide some support for the assertion that individuals with psychopathic tendencies can be identified within the community (regardless of impression management techniques) and that such tendencies are associated with specific socio-demographic characteristics.

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wenty-eight international scholars contribute 11 chapters on the key role of communication in intergroup relations. Following an introductory essay on intergroup theory and communication processes, the text focuses on specific intergroup contexts, examining communication within and between cultural, disability, age, sex and sexuality, and language groups. The remaining chapters explore the communicating of identity across communication contexts, including small group, organizational, mass, and Internet communications. The text is designed for scholars in the fields of communication and intergroup social psychology, and is also suited for use in upper- division undergraduate and introductory graduate courses in those areas. Annotation ©2004 Book News, Inc., Portland, OR

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Acoustic emission (AE) technique is a popular tool used for structural health monitoring of civil, mechanical and aerospace structures. It is a non-destructive method based on rapid release of energy within a material by crack initiation or growth in the form of stress waves. Recording of these waves by means of sensors and subsequent analysis of the recorded signals convey information about the nature of the source. Ability to locate the source of stress waves is an important advantage of AE technique; but as AE waves travel in various modes and may undergo mode conversions, understanding of the modes (‘modal analysis’) is often necessary in order to determine source location accurately. This paper presents results of experiments aimed at finding locations of artificial AE sources on a thin plate and identifying wave modes in the recorded signal waveforms. Different source locating techniques will be investigated and importance of wave mode identification will be explored.

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The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme