'Speech recognition' diaporamas de présentation

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Deep Learning from Speech Analysis/Recognition to Language/Multimodal Processing

Deep Learning from Speech Analysis/Recognition to Language/Multimodal Processing

Deep Learning from Speech Analysis/Recognition to Language/Multimodal Processing. Li Deng Deep Learning Technology Center, Microsoft Research, Redmond, WA. USA A Tutorial at Intern. Workshop on Mathematical Issues in Information Sciences (MIIS). Outline.

By jana
(363 views)

Reproducible Computational Experiments

Reproducible Computational Experiments

Reproducible Computational Experiments. Mark Liberman University of Pennsylvania http://ling.upenn.edu/~myl. Reproducible (?) Replicable Computational Experiments. Mark Liberman University of Pennsylvania http://ling.upenn.edu/~myl.

By Renfred
(724 views)

Mark Hasegawa-Johnson January 29, 2003

Mark Hasegawa-Johnson January 29, 2003

Audiovisual Display and Audiovisual Recognition in Free Field Environments: Caves, Cars, and Critical Bands. Mark Hasegawa-Johnson January 29, 2003 Collaborators: Bowon Lee, Camille Goudeseune, Zhinian Jing, Danfeng Li, Thomas Huang, Stephen Levinson. What is “free-field audio?”.

By Albert_Lan
(229 views)

Search and Decoding in Speech Recognition

Search and Decoding in Speech Recognition

Search and Decoding in Speech Recognition. Automatic Speech Recognition. Automatic Speech Recognition. Spoken language understanding is a difficult task, and it is remarkable that humans do well at it.

By mandell
(220 views)

Overview of Statistical Language Models

Overview of Statistical Language Models

Overview of Statistical Language Models. ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Outline. What is a statistical language model (SLM)? Brief history of SLM Types of SLM Applications of SLM. What is a Statistical Language Model (LM)?.

By carter
(452 views)

Mobile Dictation With Automatic Speech Recognition for Healthcare Purposes

Mobile Dictation With Automatic Speech Recognition for Healthcare Purposes

Mobile Dictation With Automatic Speech Recognition for Healthcare Purposes. Tuuli Keskinen, Aleksi Melto, Jaakko Hakulinen , Markku Turunen, Santeri Saarinen, Tamás Pallos TAUCHI research center, School of Information Sciences, University of Tampere, Finland

By tilly
(166 views)

Natural Language Processing (NLP)

Natural Language Processing (NLP)

Natural Language Processing (NLP). Prof. Carolina Ruiz Computer Science WPI. References. The essence of Artificial Intelligence By A. Cawsey Prentice Hall Europe 1998 Artificial Intelligence: Theory and Practice By T. Dean, J. Allen, and Y. Aloimonos.

By tara
(334 views)

Speech Recognition

Speech Recognition

Speech Recognition. UNIT -5. Introduction. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges ( eg ., variations of the context, speakers, and environment ).

By morey
(220 views)

BY: PRATIBHA CHANNAMSETTY SHRUTHI SAMBASIVAN

BY: PRATIBHA CHANNAMSETTY SHRUTHI SAMBASIVAN

SPEECH RECOGNITION FOR MOBILE SYSTEMS. BY: PRATIBHA CHANNAMSETTY SHRUTHI SAMBASIVAN. Introduction. What is speech recognition? Automatic speech recognition(ASR) is the process by which a computer maps an acoustic speech signal to text. CLASSIFICATION OF SPEECH RECOGNITION SYSTEM. U sers

By gram
(189 views)

NONLINEAR DYNAMIC INVARIANTS FOR CONTINUOUS SPEECH RECOGNITION

NONLINEAR DYNAMIC INVARIANTS FOR CONTINUOUS SPEECH RECOGNITION

NONLINEAR DYNAMIC INVARIANTS FOR CONTINUOUS SPEECH RECOGNITION. • Author: Daniel May Inst. for Signal and Info. Processing Dept. Electrical and Computer Eng. Mississippi State University • Contact Information: Box 9571 Mississippi State University

By regina
(149 views)

Ruang Lingkup Mengapa dan Apa Siapa Saja yang Terlibat Konsep dan Dasar Sejarah dan Paradigma IMK

Ruang Lingkup Mengapa dan Apa Siapa Saja yang Terlibat Konsep dan Dasar Sejarah dan Paradigma IMK

PENDAHULUAN. Ruang Lingkup Mengapa dan Apa Siapa Saja yang Terlibat Konsep dan Dasar Sejarah dan Paradigma IMK. Course Overview. Human abilities Evaluation (without users) Design Dialog & interaction Evaluation (with users) Special topics CSCW, InfoVis, Ubicomp, Agents.

By hester
(463 views)

Automatic Speech Recognition: An Overview

Automatic Speech Recognition: An Overview

Automatic Speech Recognition: An Overview. Julia Hirschberg CS 4706 (Thanks to Roberto Pieraccini and Francis Ganong for some slides). DIALOG. SEMANTICS. SPEECH RECOGNITION. SPOKEN LANGUAGE UNDERSTANDING. SYNTAX. LEXICON. MORPHOLOGY. SPEECH SYNTHESIS. PHONETICS. DIALOG

By abba
(166 views)

Speech enable your EHR What’s Now and What’s Next?

Speech enable your EHR What’s Now and What’s Next?

Speech enable your EHR What’s Now and What’s Next?. Problem-solving. Time spent on documentation Staff productivity Clinician burnout Expensive data entry Speech Recognition - it’s not “one size fits all” Burden of IT management. T-Pro Clinical Documentation Solution.

By luna
(373 views)

Assistive Technology Basics Teresa Goddard Lisa Mathess (800) 526-7234 (Voice)

Assistive Technology Basics Teresa Goddard Lisa Mathess (800) 526-7234 (Voice)

Assistive Technology Basics Teresa Goddard Lisa Mathess (800) 526-7234 (Voice) (877) 781-9403 (TTY) jan@askjan.org. Assistive Technology. What is Assistive Technology?

By grace
(247 views)

Improvement in the quality of automated dictation by making explicit use of semantic knowledge

Improvement in the quality of automated dictation by making explicit use of semantic knowledge

Improvement in the quality of automated dictation by making explicit use of semantic knowledge. Klaus Stanglmayr Friday, February 23, 2007. Philips Speech Recognition Systems. Philips Speech Recognition Systems. A business unit of Royal Philips Electronics

By buzz
(135 views)

Speech Recognition

Speech Recognition

Speech Recognition. Part 3 Back end processing. Speech recognition simplified block diagram. Training. Speech Capture. Feature Extraction. Models. Pattern Matching. Process Results. Text. Building a phone model. Annotate the speech input. Split and create feature vectors for each.

By beau
(164 views)

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION. Askars Salimbajevs. Supervisor: Prof. Inguna Skadiņa University of Latvia. Introduction Acoustic modelling Automatic data acquisition Pronunciation model Language modelling Text preprocessing Word and sub-word N-gram models

By Patman
(221 views)

Pardon Me, Your Computer’s Showing Using speech to speed and streamline desktop computing

Pardon Me, Your Computer’s Showing Using speech to speed and streamline desktop computing

Pardon Me, Your Computer’s Showing Using speech to speed and streamline desktop computing . Kimberly Patch President, Redstart Systems SpeechTek West February 22, 2007 . Keys to Using Speech to Speed and Streamline Computer Control Minimizing steps Making commands easy to remember

By zwi
(167 views)

Probabilistic reasoning over time

Probabilistic reasoning over time

Probabilistic reasoning over time. This sentence is likely to be untrue in the future!. The basic problem. What do we know about the state of the world now given a history of the world before . The only evidence we have are probabilities.

By annick
(126 views)

Anniversary Conference and Workshop Summary

Anniversary Conference and Workshop Summary

Anniversary Conference and Workshop Summary. PACS. DICOM services supporting PACS Realities of deploying DICOM in a PACS Transport between PACS Internationalization challenges. Expanding Modality Coverage. Widely accepted Radiology Cardiology Radiotherapy

By fritz
(125 views)

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