'Feature sets' presentation slideshows

Articulatory Feature-Based Speech Recognition

word. word. ind 1. ind 1. U 1. U 1. sync 1,2. sync 1,2. S 1. S 1. ind 2. ind 2. U 2. U 2. sync 2,3. sync 2,3. S 2. S 2. ind 3. ind 3. U 3. U 3. S 3. S 3. JHU WS06 Final team presentation August 17, 2006. Articulatory Feature-Based Speech Recognition. Project Participants.

1.23k views • 105 slides

Articulatory Feature-Based Speech Recognition

word. word. ind 1. ind 1. U 1. U 1. sync 1,2. sync 1,2. S 1. S 1. ind 2. ind 2. U 2. U 2. sync 2,3. sync 2,3. S 2. S 2. ind 3. ind 3. U 3. U 3. S 3. S 3. Articulatory Feature-Based Speech Recognition. JHU WS06 Final team presentation August 17, 2006. Project Participants.

575 views • 44 slides

CMPE 118/L Introduction to Mechatronics

598 views • 34 slides

Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources

Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources. Kate Forbes-Riley and Diane Litman Learning Research and Development Center and Computer Science Department University of Pittsburgh. Overview. Motivation spoken dialogue tutoring systems Emotion Annotation

492 views • 34 slides

IEEE PES Meeting

IEEE PES Meeting. Marc L. Hunter Foxboro A 2 Automation Platform Business Manager North American. Agenda. Discussion of DCS, PLC, SCADA, and Hybrid control systems Markets served by various technologies Hybrid control system advantages in Water and Waste Water

484 views • 28 slides

Presented by: Sandeep Dept of Computer & Information Sciences University of Delaware

Presented by: Sandeep Dept of Computer & Information Sciences University of Delaware. Detection of unknown computer worms based on behavioral classification of the host Robert Moskovitch ,Yuval Elovici ,Lior Rokach. Worms. Worms are considered malicious in nature

364 views • 25 slides

Machine Learning techniques for biomarker discovery in proteomic pattern data

Machine Learning techniques for biomarker discovery in proteomic pattern data. Elena Marchiori Department of Computer Science Vrije Universiteit Amsterdam. Overview. Proteomic pattern data How to use the data Approaches Methodology Case study Conclusion.

446 views • 22 slides

Feature Driven Development

Feature Driven Development. Satish , Arjun. Contents. What & Why of FDD Feature Definition Project Roles & Responsibilities FDD Process FDD Project Progress Conclusions. What is FDD ?. Highly Iterative Emphasizes Quality at each step Delivers frequent, tangible, working results

838 views • 21 slides

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE. Truc-Vien T. Nguyen Lab: Named Entity Recognition. Download. Slides http://sites.google.com/site/trucviennguyen/Lab NER -- Vien.pdf Software http://sites.google.com/site/trucviennguyen/Teaching/AI/SSHSecureShellClient-3.2.9.rar.

327 views • 19 slides

Cisco IOS & Router Config

Cisco IOS & Router Config. Semester 2V2 Chapter 7 Chapter 8. Routers boot Cisco IOS software from:. Flash memory TFTP server ROM (not full Cisco IOS software). Cisco IOS software allows you to use several alternatives to loading your system. (router)

437 views • 18 slides

UNIFICATION GRAMMAR

312 views • 15 slides

Conditional Random Fields

Conditional Random Fields. A form of discriminative modelling Has been used successfully in various domains such as part of speech tagging and other Natural Language Processing tasks Processes evidence bottom-up Combines multiple features of the data

255 views • 14 slides

Popular Models

Best Cisco ISR G2 router families from smallest to largest form factor. The new features in Cisco ISR G2 include video streaming and on-demand services support. Deployment and maintenance processes have been simplified, and network performance and energy management improved over the previous generation of routers.

177 views • 9 slides

TuneAttic Business Plan, Confidential

360 Degree Music Service Assessment. Mark Mulligan December 2011. TuneAttic Business Plan, Confidential. Page 1. TuneAttic Business Plan, Confidential. Page 1. Overview. The 360 Music Service Assessment is a holistic expert review tool that deploys a user needs based approach to address:

202 views • 9 slides

Innovation Workout

259 views • 8 slides

OASIS Symposium 2006: The Meaning of Interoperability

143 views • 3 slides

110 views • 1 slides

Fluency in Information Technology

Fluency in Information Technology. Desktop PUBLISHING with ms wORD. Data analysos and visualization with ms excel. Making information Available to the world with html and web pages. PRESENTATION WITH MS POWERPOINT. Visual programming with alice.

166 views • 1 slides

Exploring Unsupervised and Knowledge-Rich Approaches to Coreference Resolution.

Exploring Unsupervised and Knowledge-Rich Approaches to Coreference Resolution. Nathan Gilbert & Ellen Riloff University of Utah. Introduction. The current state of the art. Unsupervised Learning.

139 views • 1 slides

Daniel J. Garcia, Mentors: Dr. Lawrence Hall, Dr. Dmitry Goldgof, Kurt Kramer

Start. Generate 200 random feature sets. Run 10 fold cross validation on each set random set. Sort results of 10-fold cross validation by training time. Select 9 fastest random feature sets. Create three new sets: Union fastest 3, union fastest 5 and Union Fastest 9.

112 views • 1 slides