Command Me Specification

Slides:



Advertisements
Similar presentations
Presented by Erin Palmer. Speech processing is widely used today Can you think of some examples? Phone dialog systems (bank, Amtrak) Computers dictation.
Advertisements

Introduction to Computational Linguistics
Sean Powers Florida Institute of Technology ECE 5525 Final: Dr. Veton Kepuska Date: 07 December 2010 Controlling your household appliances through conversation.
Speech Recognition Problem and Hidden Markov Model Ziba Rostamian CS Winter 2008.
SPEECH RECOGNITION Kunal Shalia and Dima Smirnov.
Speech Translation on a PDA By: Santan Challa Instructor Dr. Christel Kemke.
Shallow Processing: Summary Shallow Processing Techniques for NLP Ling570 December 7, 2011.
Big Ideas in Cmput366. Search Blind Search Iterative deepening Heuristic Search A* Local and Stochastic Search Randomized algorithm Constraint satisfaction.
Programming Logic and Design, Introductory, Fourth Edition1 Understanding Computer Components and Operations (continued) A program must be free of syntax.
Auditory User Interfaces
CS 188: Artificial Intelligence Fall 2009 Lecture 19: Hidden Markov Models 11/3/2009 Dan Klein – UC Berkeley.
Statistical Natural Language Processing. What is NLP?  Natural Language Processing (NLP), or Computational Linguistics, is concerned with theoretical.
Some Voice Enable Component Group member: CHUAH SIONG YANG LIM CHUN HEAN Advisor: Professor MICHEAL Project Purpose: For the developers,
Introduction to Automatic Speech Recognition
1 “ Speech ” EMPOWERED COMPUTING Greenfield Business Centre, 20 th September, 2006.
Rosetta Stone Course Online. Type in your account web-address: Enter your Username and Password Select Sign In Click.
Selecting and Combining Tools F. Duveau 02/03/12 F. Duveau 02/03/12 Chapter 14.
1 7-Speech Recognition (Cont’d) HMM Calculating Approaches Neural Components Three Basic HMM Problems Viterbi Algorithm State Duration Modeling Training.
Speaker Recognition By Afshan Hina.
Supervisor: Dr. Eddie Jones Electronic Engineering Department Final Year Project 2008/09 Development of a Speaker Recognition/Verification System for Security.
(CMSC5720-1) MSC projects by Prof K.H. Wong (21 July2014) (shb907) MSC projects supervised by Prof.
Machine Learning in Spoken Language Processing Lecture 21 Spoken Language Processing Prof. Andrew Rosenberg.
By: Meghal Bhatt.  Sphinx4 is a state of the art speaker independent, continuous speech recognition system written entirely in java programming language.
Lecture 10: 8/6/1435 Machine Learning Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
Machine Translation  Machine translation is of one of the earliest uses of AI  Two approaches:  Traditional approach using grammars, rewrite rules,
Voice Recognition (Presentation 2) By: Priya Devi A. S/W Developer, Xsys technologies Bangalore.
Math 5 Professor Barnett Timothy G. McManus Anthony P. Pastoors.
Sequence Models With slides by me, Joshua Goodman, Fei Xia.
1 CS 430: Information Discovery Lecture 22 Non-Textual Materials: Informedia.
Research Topics CSC Parallel Computing & Compilers CSC 3990.
Speech, Perception, & AI Artificial Intelligence CMSC February 13, 2003.
Dirk Van CompernolleAtranos Workshop, Leuven 12 April 2002 Automatic Transcription of Natural Speech - A Broader Perspective – Dirk Van Compernolle ESAT.
Audio processing methods on marine mammal vocalizations Xanadu Halkias Laboratory for the Recognition and Organization of Speech and Audio
Speech Recognition with CMU Sphinx Srikar Nadipally Hareesh Lingareddy.
Performance Comparison of Speaker and Emotion Recognition
Basic structure of sphinx 4
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.
BY KALP SHAH Sentence Recognizer. Sphinx4 Sphinx4 is the best and versatile recognition system. Sphinx4 is a speech recognition system which is written.
Reducing uncertainty in speech recognition Controlling mobile devices through voice activated commands Neil Gow, GWXNEI001 Stephen Breyer-Menke, BRYSTE003.
VoiceXML – Speech Recognition Yousef Rabah. VoiceXML Markup Language Dialogs Dependencies Standalone Vs. Hosted Speaker Dependent Vs. Speaker Independent.
Automated Speach Recognotion Automated Speach Recognition By: Amichai Painsky.
Speech Recognition Created By : Kanjariya Hardik G.
Message Source Linguistic Channel Articulatory Channel Acoustic Channel Observable: MessageWordsSounds Features Bayesian formulation for speech recognition:
1 7-Speech Recognition Speech Recognition Concepts Speech Recognition Approaches Recognition Theories Bayse Rule Simple Language Model P(A|W) Network Types.
Background & Related Work Approaches to teaching media computation have so far primarily been reliant on textual programming languages [1]. For students.
By: Nicole Cappella. Why I chose Speech Recognition  Always interested me  Dr. Phil Show Manti Teo Girlfriend Hoax  Three separate voice analysts proved.
Machine Language Computer languages cannot be directly interpreted by the computer – they are not in binary. All commands need to be translated into binary.
Tasneem Ghnaimat. Language Model An abstract representation of a (natural) language. An approximation to real language Assume we have a set of sentences,
Siri Voice controlled Virtual Assistant Haroon Rashid Mithun Bose 18/25/2014.
Speech Recognition
Recurrent Neural Networks for Natural Language Processing
Artificial Intelligence for Speech Recognition
A presentation on Basics of Speech Recognition Systems
Conditional Random Fields for ASR
Natural Language Processing (NLP)
3.0 Map of Subject Areas.
Measuring Sustainability Reporting using Web Scraping and Natural Language Processing Alessandra Sozzi
EEL 4713/EEL 5764 Computer Architecture
Graph Paper Programming
Assistive System Progress Report 1
Alexa Programming.
Language and Statistics
Internet and Community Resources
Voice Activation for Wealth Management
Natural Language Processing (NLP)
Word2Vec.
Type Topic in here! Created by Educational Technology Network
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems NDSS 2019 Hadi Abdullah, Washington Garcia, Christian Peeters, Patrick.
Natural Language Processing (NLP)
Presentation transcript:

Command Me Specification Shawn Mathew, Anthony Tan, Amy Wong

Background Information - Speech Recognition Automatic speech recognition systems takes a speech signal and converts into words for natural processing language Process: Digital Sampling Acoustic Signal Processing Recognition of Words (Hidden Markov Modeling) hidden Markov model is a tool for representing probability distributions over sequences of observations

Background Information - Natural Language Processing What is natural language? Natural language is stated to be any language that are formed by humans and has evolved naturally through human use. It can be categorized as speech, writing, or signing (sign language). What is natural language processing? The study of this language and its interaction with computers.

Researched uses for Natural Language Processing Algorithms These are some of the tasks that have been researched for Natural Language Processing Summarizing chunks of text Translating one human language into another Converting information that is understandable to computers into readable language for humans Determining the part of speech of a word Converting images of text into machine understandable text

Project Idea Our project idea revolves around taking a verified human speech and converting that into an action that will be performed by a computer. Step 1: Provide computer with a sound clip from a person speaking. Step 2: Convert that sound representation into text. Step 3: The computer will perform a certain task/action based on the textual representation that was derived from human speech For now, we are focusing on commands for a computer such opening a certain program or searching what the weather is like through the web Over time, we may improve through machine learning such that the computer will learn new commands that are given to it instead of choosing from a set of predetermined tasks.

Project Implementations Take a saying and the speaker’s voice from microphone Turn the saying into text Text needs to be tokenized Tokenized text should be passed into machine learning algorithm or set of if statements Algorithm will decide what should be executed

Parsing Text

Algorithm After Tokenizer Machine Learning? Train the machine If Statements? Key words connected with commands

Additional Implementation Add voice recognition for user Commands may change according to user

Libraries Bob.spear (Speaker recognition Python toolkit) CMU Sphinx (Voice to Text) Natural Language Toolkit (Tokenizer) Keras (Machine Learning) Chatterbox (take text and provide response)

Sources http://www.ll.mit.edu/mission/cybersec/publications/publication- files/full_papers/020513_Reynolds.pdf https://www.ll.mit.edu/publications/journal/pdf/vol03_no1/3.1.3.speechrecognitio n.pdf http://mi.eng.cam.ac.uk/~mjfg/mjfg_NOW.pdf http://blog.algorithmia.com/introduction-natural-language-processing-nlp/ https://en.wikipedia.org/wiki/Natural_language_processing https://en.wikipedia.org/wiki/Speech_recognition