Presentation is loading. Please wait.

Presentation is loading. Please wait.

1 st State T 2 nd State T 3 rd State T “[t]” Phoneme “track” “down” “and” “neutralize” “terrorists” “Track down and neutralize terrorists.” Word Sentence.

Published byMeryl Lawson Modified over 8 years ago

Similar presentations

Presentation on theme: "1 st State T 2 nd State T 3 rd State T “[t]” Phoneme “track” “down” “and” “neutralize” “terrorists” “Track down and neutralize terrorists.” Word Sentence."— Presentation transcript:

1 1 st State T 2 nd State T 3 rd State T “[t]” Phoneme “track” “down” “and” “neutralize” “terrorists” “Track down and neutralize terrorists.” Word Sentence “[t]”“[r]”“[ae]”“[k]” “Track[t/r/ae/k]” Track (TR) turn come Left (  ) Down (↓) Right (  ) here there............ P(down|track) P(left|track) P(right|track) P(here|track) P(there|track) and (&) now quickly...... P(and|down) P(now|down) P(quickly||down) … … … Isolated symbolConnected symbolSentence “T” “R”  ↓ Direction: … “TR” “↓”“↓” “&”“&” Speech : “Track down and neutralize Terrorists.” Hand signal: “TR / ↓ / & / NEU / TER”

2 UG training DB based on moveme HMM UG # N …… HMM UG # 2 HMM WORD # M …… HMM UG # 2 …… UG HMM # N UG HMM # 2 UG HMM # 1 …… WORD HMM # M UG HMM # 3UG HMM # 11 UG HMM # 3UG HMM # 11 WORD HMM # 2 UG HMM # 3UG HMM # 11 WORD HMM # 1 UG HMM # 3UG HMM # 11 ……

3 UG training DB based on moveme …… UG HMM # N UG HMM # 2 UG HMM # 1 …… WORD HMM # M UG HMM # 3UG HMM # 11 UG HMM # 3UG HMM # 11 WORD HMM # 2 UG HMM # 3UG HMM # 11 WORD HMM # 1 UG HMM # 3UG HMM # 11 …… Pre-processing - Tracking hand - Feature extraction Recognition - Decoding word sequence Image sequence Word network Track (TR) turn come Left (  ) Down (↓) Right (  ) here there............ P(down|track) P(left|track) P(right|track) P(here|track) P(there|track) and (&) now quickly...... P(and|down) P(now|down) P(quickly||down) … … …

4 UG training DB based on moveme …… UG HMM # N UG HMM # 2 UG HMM # 1 …… WORD HMM # M UG HMM # 3UG HMM # 11 UG HMM # 3UG HMM # 11 WORD HMM # 2 UG HMM # 3UG HMM # 11 WORD HMM # 1 UG HMM # 3UG HMM # 11 …… Pre-processing - Tracking hand - Feature extraction Recognition - Decoding word sequence Image sequence Word network Track (TR) turn come Left (  ) Down (↓) Right (  ) here there............ P(down|track) P(left|track) P(right|track) P(here|track) P(there|track) and (&) now quickly...... P(and|down) P(now|down) P(quickly||down) … … … Word description Word index Merging rule Meaning Step 1Step 2 WD01UG # 3UG # 11come WD02UG # 2UG # 12there ………right WD01 WD23 WD09 … COME ME QUIET … Recognized command:

5 UG training DB based on moveme …… UG HMM # N UG HMM # 2 UG HMM # 1 …… WORD HMM # M UG HMM # 3UG HMM # 11 UG HMM # 3UG HMM # 11 WORD HMM # 2 UG HMM # 3UG HMM # 11 WORD HMM # 1 UG HMM # 3UG HMM # 11 …… Pre-processing - Tracking hand - Feature extraction Recognition - Decoding word sequence Word network Word description Image sequence WD01 WD23 WD09 … COME ME QUIET … Recognized command: Track (TR) turn come Left (  ) Down (↓) Right (  ) here there............ P(down|track) P(left|track) P(right|track) P(here|track) P(there|track) and (&) now quickly...... P(and|down) P(now|down) P(quickly||down) … … … Word index Merging rule Meaning Step 1Step 2 WD01UG # 3UG # 11come WD02UG # 2UG # 12there ………right

6 UG training DB based on moveme …… UG HMM # N UG HMM # 2 UG HMM # 1 …… WORD HMM # M UG HMM # 3UG HMM # 11 UG HMM # 3UG HMM # 11 WORD HMM # 2 UG HMM # 3UG HMM # 11 WORD HMM # 1 UG HMM # 3UG HMM # 11 …… Pre-processing - Tracking hand - Feature extraction Recognition - Decoding word sequence Word network Word description Image sequence WD01 WD23 WD09 … COME ME QUIET … Recognized command: Track (TR) turn come Left (  ) Down (↓) Right (  ) here there............ P(down|track) P(left|track) P(right|track) P(here|track) P(there|track) and (&) now quickly...... P(and|down) P(now|down) P(quickly||down) … … … Word index Merging rule Meaning Step 1Step 2 WD01UG # 3UG # 11come WD02UG # 2UG # 12there ………right

Download ppt "1 st State T 2 nd State T 3 rd State T “[t]” Phoneme “track” “down” “and” “neutralize” “terrorists” “Track down and neutralize terrorists.” Word Sentence."

Similar presentations

Module 4 Lesson 1.

Module 4 Lesson 1.
1 st Place Post-Secondary Winner. 2 nd Place Post-Secondary Winner.

1 st Place Post-Secondary Winner. 2 nd Place Post-Secondary Winner.
Speech Recognition Part 3 Back end processing. Speech recognition simplified block diagram Speech Capture Speech Capture Feature Extraction Feature Extraction.

Speech Recognition Part 3 Back end processing. Speech recognition simplified block diagram Speech Capture Speech Capture Feature Extraction Feature Extraction.
Speech in Multimedia Hao Jiang Computer Science Department Boston College Oct. 9, 2007.

Speech in Multimedia Hao Jiang Computer Science Department Boston College Oct. 9, 2007.
Speech Recognition. What makes speech recognition hard?

Speech Recognition. What makes speech recognition hard?
Communications & Multimedia Signal Processing Analysis of the Effects of Train noise on Recognition Rate using Formants and MFCC Esfandiar Zavarehei Department.

Communications & Multimedia Signal Processing Analysis of the Effects of Train noise on Recognition Rate using Formants and MFCC Esfandiar Zavarehei Department.
Hidden Markov Models. Hidden Markov Model In some Markov processes, we may not be able to observe the states directly.

Hidden Markov Models. Hidden Markov Model In some Markov processes, we may not be able to observe the states directly.
Recognition of Human Gait From Video Rong Zhang, C. Vogler, and D. Metaxas Computational Biomedicine Imaging and Modeling Center Rutgers University.

Recognition of Human Gait From Video Rong Zhang, C. Vogler, and D. Metaxas Computational Biomedicine Imaging and Modeling Center Rutgers University.
Doug Downey, adapted from Bryan Pardo,Northwestern University

Doug Downey, adapted from Bryan Pardo,Northwestern University
Why is ASR Hard? Natural speech is continuous

Why is ASR Hard? Natural speech is continuous
Before we get started… topics for later Lexile Outside Reading Programs Assessment/Intervention Resources Recommended Beginning of Year Assessments Parent.

Before we get started… topics for later Lexile Outside Reading Programs Assessment/Intervention Resources Recommended Beginning of Year Assessments Parent.
Final Presentation. Lale AkarunOya Aran Alexey Karpov Milos Zeleny Hasim Sak Erinc Dikici Alp Kindiroglu Marek Hruz Pavel Campr Daniel Schorno Alexander.

Final Presentation. Lale AkarunOya Aran Alexey Karpov Milos Zeleny Hasim Sak Erinc Dikici Alp Kindiroglu Marek Hruz Pavel Campr Daniel Schorno Alexander.
Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley.

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley.
Isolated-Word Speech Recognition Using Hidden Markov Models

Isolated-Word Speech Recognition Using Hidden Markov Models
1 7-Speech Recognition (Cont’d) HMM Calculating Approaches Neural Components Three Basic HMM Problems Viterbi Algorithm State Duration Modeling Training.

1 7-Speech Recognition (Cont’d) HMM Calculating Approaches Neural Components Three Basic HMM Problems Viterbi Algorithm State Duration Modeling Training.
Source A Advance to next slide Advance to source material Click for help EndPPT.

Source A Advance to next slide Advance to source material Click for help EndPPT.
A brief overview of Speech Recognition and Spoken Language Processing Advanced NLP Guest Lecture August 31 Andrew Rosenberg.

A brief overview of Speech Recognition and Spoken Language Processing Advanced NLP Guest Lecture August 31 Andrew Rosenberg.
By: Meghal Bhatt.  Sphinx4 is a state of the art speaker independent, continuous speech recognition system written entirely in java programming language.

By: Meghal Bhatt.  Sphinx4 is a state of the art speaker independent, continuous speech recognition system written entirely in java programming language.
Where hands say it all.  Start with demo to illustrate our idea.

Where hands say it all.  Start with demo to illustrate our idea.
CVPR Workshop on RTV4HCI 7/2/2004, Washington D.C. Gesture Recognition Using 3D Appearance and Motion Features Guangqi Ye, Jason J. Corso, Gregory D. Hager.

CVPR Workshop on RTV4HCI 7/2/2004, Washington D.C. Gesture Recognition Using 3D Appearance and Motion Features Guangqi Ye, Jason J. Corso, Gregory D. Hager.

Similar presentations

Ads by Google