Math 5 Professor Barnett Timothy G. McManus Anthony P. Pastoors.

Slides:



Advertisements
Similar presentations
Lecture 51 The Telephone System. Lecture 52 The Telephone System The modern telephone system draws from these Electrical Engineering subdisciplines: Signal.
Advertisements

Digital Systems: Introductory Concepts Wen-Hung Liao, Ph.D.
Speech in Multimedia Hao Jiang Computer Science Department Boston College Oct. 9, 2007.
Interfacing with the Analog World Wen-Hung Liao, Ph.D.
D SP InputDigital Processing Output Algorithms Typical approach to DASP systems development Algorithms are in the focus at development of any digital signal.
Input to the Computer * Input * Keyboard * Pointing Devices
Natural Language Processing - Speech Processing -
6/3/20151 Voice Transformation : Speech Morphing Gidon Porat and Yizhar Lavner SIPL – Technion IIT December
Voice Recognition Technology Kathleen Kennedy COMP 1631 Winter 2010.
Chapter 2: Pattern Recognition
EE2F1 Speech & Audio Technology Sept. 26, 2002 SLIDE 1 THE UNIVERSITY OF BIRMINGHAM ELECTRONIC, ELECTRICAL & COMPUTER ENGINEERING Digital Systems & Vision.
Fig. 2 – Test results Personal Memory Assistant Facial Recognition System The facial identification system is divided into the following two components:
COMP 4060 Natural Language Processing Speech Processing.
Chapter 8. Linear Systems with Random Inputs 1 0. Introduction 1. Linear system fundamentals 2. Random signal response of linear systems Spectral.
Real-Time Speech Recognition Thang Pham Advisor: Shane Cotter.
A PRESENTATION BY SHAMALEE DESHPANDE
Hossein Sameti Department of Computer Engineering Sharif University of Technology.
Ameneh Sadat Yazdaninik
Signal ProcessingES & BM MUET1 Lecture 2. Signal ProcessingES & BM MUET2 This lecture Concept of Signal Processing Introduction to Signals Classification.
Knowledge Base approach for spoken digit recognition Vijetha Periyavaram.
Artificial Intelligence 2004 Speech & Natural Language Processing Natural Language Processing written text as input sentences (well-formed) Speech.
Input Devices Manual and Automatic By Laura and Gracie.
Supervisor: Dr. Eddie Jones Electronic Engineering Department Final Year Project 2008/09 Development of a Speaker Recognition/Verification System for Security.
Purpose of study A high-quality computing education equips pupils to use computational thinking and creativity to understand and change the world. Computing.
Input Devices.  Identify audio and video input devices  List the function of the respective devices.
CP SC 881 Spoken Language Systems. 2 of 23 Auditory User Interfaces Welcome to SLS Syllabus Introduction.
Acoustic Analysis of Speech Robert A. Prosek, Ph.D. CSD 301 Robert A. Prosek, Ph.D. CSD 301.
CMPD273 Multimedia System Prepared by Nazrita Ibrahim © UNITEN2002 Multimedia System Characteristic Reference: F. Fluckiger: “Understanding networked multimedia,
NEURAL NETWORKS FOR DATA MINING
BIOMETRICS By: Lucas Clay and Tim Myers. WHAT IS IT?  Biometrics are a method of uniquely identifying a person based on physical or behavioral traits.
Voice Recognition All Talk No Walk.
Jun-Won Suh Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering Speaker Verification System.
Artificial Intelligence 2004 Speech & Natural Language Processing Natural Language Processing written text as input sentences (well-formed) Speech.
Speaker Recognition by Habib ur Rehman Abdul Basit CENTER FOR ADVANCED STUDIES IN ENGINERING Digital Signal Processing ( Term Project )
MULTIMEDIA INPUT / OUTPUT TECHNOLOGIES INTRODUCTION 6/1/ A.Aruna, Assistant Professor, Faculty of Information Technology.
Fourier Analysis of Discrete-Time Systems
Speaker Verification System in a Security Application HŪDATBrian Bash Thomas Jonell Dustin Williams Advisor Dr. Les Thede.
Submitted By: Santosh Kumar Yadav (111432) M.E. Modular(2011) Under the Supervision of: Mrs. Shano Solanki Assistant Professor, C.S.E NITTTR, Chandigarh.
Creating User Interfaces Directed Speech. XML. VoiceXML Classwork/Homework: Sign up to be Voxeo developer. Do tutorials.
ECE 5525 Osama Saraireh Fall 2005 Dr. Veton Kepuska
Audio processing methods on marine mammal vocalizations Xanadu Halkias Laboratory for the Recognition and Organization of Speech and Audio
Objective 3.02 Train the system and input simple documents using speech writing techniques.
CSCI-100 Introduction to Computing Hardware Part II.
Digital imaging By : Alanoud Al Saleh. History: It started in 1960 by the National Aeronautics and Space Administration (NASA). The technology of digital.
Computer Basics SystemsViruses Alternative Input Speech.
Introduction Part I Speech Representation, Models and Analysis Part II Speech Recognition Part III Speech Synthesis Part IV Speech Coding Part V Frontier.
ARTIFICIAL INTELLIGENCE FOR SPEECH RECOGNITION. Introduction What is Speech Recognition?  also known as automatic speech recognition or computer speech.
Encoding How is information represented?. Way of looking at techniques Data Medium Digital Analog Digital Analog NRZ Manchester Differential Manchester.
Ghent University Pattern recognition with CNNs as reservoirs David Verstraeten 1 – Samuel Xavier de Souza 2 – Benjamin Schrauwen 1 Johan Suykens 2 – Dirk.
Speaker Verification System Middle Term Presentation Performed by: Barak Benita & Daniel Adler Instructor: Erez Sabag.
What is Input?  Input  Processing  Output  Storage Everything we enter into the computer to do is Input.
Speech Recognition Created By : Kanjariya Hardik G.
Pulse Code Modulation (PCM) Analog voice data must be translated into a series of binary digits before they can be transmitted. With Pulse Code Modulation.
Data and Signals & Analouge Signaling
 Speech signal processing Speech recognition Speech synthesis Speech compression Speaker diarization and its applications  Image processing Image processing.
PREPARED BY MANOJ TALUKDAR MSC 4 TH SEM ROLL-NO 05 GUKC-2012 IN THE GUIDENCE OF DR. SANJIB KR KALITA.
Speech Recognition
COMPUTER NETWORKS and INTERNETS
ARTIFICIAL NEURAL NETWORKS
Artificial Intelligence for Speech Recognition
A presentation on Basics of Speech Recognition Systems
Bits and Pieces November 6, 2007.
UNIT 5. Linear Systems with Random Inputs
Command Me Specification
CSE 313 Data Communication
Introduction to Digital Design Concepts
TIME-BASED HYBRID ANALOG-DIGITAL COMPUTATION
Keyword Spotting Dynamic Time Warping
Auditory Morphing Weyni Clacken
ROBOT CONTROL WITH VOICE
Presentation transcript:

Math 5 Professor Barnett Timothy G. McManus Anthony P. Pastoors

 Voice recognition is "the technology by which sounds, words or phrases spoken by humans are converted into electrical signals, and these signals are transformed into coding patterns to which meaning has been assigned"  Concept could more generally be called "sound recognition", we focus here on the human voice because we most often and most naturally use our voices to communicate our ideas to others in our immediate surroundings.

“ Template matching"  simplest technique and has the highest accuracy when used properly, but it also suffers from the most limitations.  The electrical signal from the microphone is digitized by an "analog-to-digital (A/D) converter", and is stored in memory.  To determine the "meaning" of this voice input, the computer attempts to match the input with a digitized voice sample, or template, that has a known meaning.  The program contains the input template, and attempts to match this template with the actual input using a simple conditional statement. “Feature analysis “Feature analysis "  “Speaker-independent" voice recognition. Does not need to find an exact or near-exact match between the actual voice input and a previously stored voice template  Processes the voice input using "Fourier transforms" or "linear predictive coding (LPC)"  Attempts to find characteristic similarities between the expected inputs and the actual digitized voice input.  These similarities will be present for a wide range of speakers, and so the system need not be trained by each new user.

 Military  Police  People with Disabilities  Health Care  Vehicle use  Security

 The difficulty in using voice as an input to a computer simulation lies in the fundamental differences between human speech and the more traditional forms of computer input. While computer programs are commonly designed to produce a precise and well- defined response upon receiving the proper input, the human voice and spoken words are anything but precise. Each human voice is different, and identical words can have different meanings if spoken with different inflections or in different contexts.