Speaker notes 440 Hello‚ fellow coworkers my name is Vanessa Williams and I will be presenting you with information regarding a vulnerable population. The vulnerable population I will discuss is the African American population and their perceived mistrust with the healthcare system. I believe this is an important topic to discuss because the African American population has the highest incidence of colorectal cancer however‚ they are not seeking healthcare as they should. We have often questioned
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this event as I am so excited to speak before you today. Not so long ago‚ I was seating exactly where you are right now and hearing an inspirational talk from a guest speaker back then. Yes‚ I am but a proud alumna of T. Paez and soon you too will be part of the Alumni. I never really imagined myself addressing you for your Recognition Day. I never thought I could be the right person to inspire you today. Nevertheless‚ here I am‚ years after I made my first wise choice. That choice back in 1981 gave
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Non-negative Matrix Factorization Based Noise Reduction for Noise Robust Automatic Speech Recognition Seon Man Kim1‚ Ji Hun Park1‚ Hong Kook Kim1‚*‚ Sung Joo Lee2‚ and Yun Keun Lee2 1 School of Information and Communications Gwangju Institute of Science and Technology‚ Gwangju 500-712‚ Korea {kobem30002‚jh_park‚hongkook}@gist.ac.kr 2 Speech/Language Information Research Center Electronics and Telecommunications Research Institute‚ Daejeon 305-700‚ Korea {lee1862‚yklee}@etri.re.kr Abstract
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driver‚ telling jokes and asking questions intended to determine whether the driver can respond alertly enough. Assuming the IBM approach‚ an artificial passenger would use a microphone for the driver and a speech generator and the vehicle ’s audio speakers to converse with the driver. The conversation would be based on a personalized profile of the driver. A camera could be used to evaluate the driver ’s "facial state" and a voice analyzer to evaluate whether the driver was becoming drowsy. If a driver
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ARTIFICIAL PASSENGER A SEMINAR REPORT Submitted by RITESH KAMAL in partial fulfillment of requirement of the Degree of Bachelor of Technology (B.Tech) IN COMPUTER SCIENCE AND ENGINEERING SCHOOL OF ENGINEERING COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY KOCHI- 682022 AUGUST 2008 DIVISION OF COMPUTER SCIENCE AND ENGINEERING SCHOOL OF ENGINEERING COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY KOCHI-682022 Certificate Certified that this is a bonafide record of the seminar
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ARTIFICIAL PASSENGER-An interactive Artificial Intelligence Anushree N.S.‚ L.Kavyashree 5th semester‚ department of ISE ‚BITM ‚ Bellary. Abstract-Usually‚ an automobilist interacts with the other passengers to stay alert during a journey. This methodology may not actually help him to the mark. But‚ an application of the artificial intelligence can fix the issue. It is called as “Artificial
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------------------------------------------------- EIR Lecture - 2012 ------------------------------------------------- Mr. Albert Chew [Author] HMGT 3700 Dr. Josiam Faculty‚ Staff and Students of the University of North Texas’ Department of Hospitality and Tourism Management had high expectations when they gathered at the Gateway on October 17th. Mr. Albert Chew‚ Chief Operating Officer for Freeman Company’s expo division‚ and the Department’s 37th Executive In Residence was not about
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A Miller Prof. Gould ENG 262 12-3-11 Recognition and Reversal: Othello Aristotle classifies both recognitions and reversals as the greatest point of tragedy in a play or story. Recognitions and reversals are consistently used to develop character‚ advance the plot‚ and get a reaction of pity and fear from the audience. Recognition is the act of realization or knowledge or feeling that someone or something present has been encountered before. Reversals
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Negative Speaker’s Roles: (Affirmative 1st Speech) 1st Speech 1. Do you accept the Affirmative definition? If no‚ explain why :( in a PROPER PARAGRAPH) e.g. (NO COPYING): we the negative team accepts this definition/disagrees with this definition because… 2. Rebuttal: This CAN NOT is written before the debate. Rebuttal is when you say why they were wrong and they do the same. You have to look for as many points as you can to say they are wrong (but they cannot be random e.g. vocabulary mistakes)
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