'Recognition rate' presentation slideshows

CS479/679 Pattern Recognition Dr. George Bebis

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Speech Assessment: Methods and Applications for Spoken Language Learning

Speech Assessment: Methods and Applications for Spoken Language Learning. J.-S. Roger Jang ( ??? ) jang@cs.nthu.edu.tw http://www.cs.nthu.edu.tw/~jang Multimedia Information Retrieval Lab CS Dept, Tsing Hua Univ, Taiwan. Outline. Introduction to speech assessment Methods

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CS 4705

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Comparison of machine and human recognition of isolated instrument tones

Comparison of machine and human recognition of isolated instrument tones. Ichiro Fujinaga McGill University. Overview. Introduction Exemplar-based learning k-NN classifier Genetic algorithm Machine recognition experiments Comparison with human performance Conclusions. Introduction.

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A robust automated attendance system using face recognition techniques PhD proposal; May 2009

A robust automated attendance system using face recognition techniques PhD proposal; May 2009 Gawed Nagi Supervisor Dr. Rahmita Wirza Supervisory committee Dr. Muhamad Taufik Dr. Fatimah Khalid. Contents. Introduction Background Problem Statement Research objectives

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ETSI STQ Aurora Distributed Speech Recognition (DSR)

ETSI STQ Aurora Distributed Speech Recognition (DSR). Distributed Speech Recognition. Dieter Kopp Alcatel Research & Innovation email:Dieter.Kopp@alcatel.de. DSR system vision. ETSI STQ Aurora. Participants

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9. Interaction Devices

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Face Recognition based on Kernel Radial Basis Function

Neural Networks. Face Recognition based on Kernel Radial Basis Function. Ali Razmkhah Dr. Ebrahimi. 9 Jan 2010 Sahand University of Technology. Introduction. Face recognition is a very challenging research problem due to variations in

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Introduction

Avoiding Segmentation in Multi-digit Numeral String Recognition by Combining Single and Two-digit Classifiers Trained without Negative Examples. Dan Ciresan Politehnica University of Timisoara Computer Department Timisoara, Romania dan.ciresan@ac.upt.ro. Introduction.

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Analysis of the Effects of Train noise on Recognition Rate using Formants and MFCC

Analysis of the Effects of Train noise on Recognition Rate using Formants and MFCC. Esfandiar Zavarehei Department of Electronic and Computer Engineering Brunel University 28 January, 2004. Contents. The effect of noise on LP-Model Poles Formant Extraction using LP-Model of speech

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Human Capabilities on Video-based Facial Expression Recognition

Human Capabilities on Video-based Facial Expression Recognition. Motivation. Facial Expression Recognition goal: human-like man-machine communication six universal facial expressions [Ekman]: anger, disgust, fear, happiness, sadness, surprise minimal muscle activity

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Feature Selection for Pattern Recognition

Machine Learning Feature Selection. Feature Selection for Pattern Recognition. J.-S. Roger Jang ( ??? ) CSIE Dept., National Taiwan University ( ???? ????? ) http://mirlab.org/jang jang@mirlab.org. Feature Selection: Goal & Benefits. Feature selection Also known as input selection

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CSCI1412 Lecture 12 Q & A

phones off (please). CSCI1412 Lecture 12 Q & A. Interfacing and I/O Peripherals Dr John Cowell. Q 1. Which of the following is NOT a true statement about PCIe PCIe can transit data in both directions at the same time. Physical PCIe slots may contain 1,2,4,8,16.32 lanes.

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