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By:   Khalid El-Darymli  G0327887 Speech to Sign Language Interpreter System Supervisor:   Dr. Othman O. Khalifa International Islamic University Malaysia Kulliyyah of Engineering, ECE Dept.
OUTLINE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Statement ,[object Object],[object Object],[object Object],! IS IT FAIR ?
RESEARCH GOAL AND OBJECTIVES   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Main Parts of Speech to Sign Language Interpreter System Speech-Recognition  Engine ASL pre-recorded  Video-clips Database Recognized Text ASL Translation Continuous Input Speech Recognized Text
Automatic Speech Recognition ( ASR ): ,[object Object],[object Object],SR Engine Recognized Text Input Voice
The Structure of SR Engine (LVCSR) Signal  Processing AM P ( A 1 , …, A T  | P 1 ,… , P k ) Dictionary P ( P 1 , P 2 , …, P k  | W ) LM P ( W n  | W 1 , …, W n-1 ) X={x 1 ,x 2 , …, x T  } Hypothesis  Evaluation Decoder P(X | W)*P(W) TRAINING DECODING Best  Hypotheses H = {W 1 , W 2 , …, W k } W BEST Input Audio
SIGNAL PROCESSING (FRONT-END)  : Pre-emphasis Framing Windowing Speech  waveform  y[n] y t ` [n] Power Spectrum  Calculation y t [n] Mel  Filterbank S t [k] ln| | 2 IDFT 13 c t [n] 13   c t [n] 13  c t [n] x[n] , 16-bits  integer data S t [m] Pre-emphasis    is the pre-emphasis parameter. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Speech waveform of a phoneme “e” ,[object Object],After pre-emphasis and Hamming windowing Power spectrum MFCC
TRAINING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],AM P ( A 1 , …, A T  | P 1 ,… , P k ) Dictionary P ( P 1 , P 2 , …, P k  | W ) LM P ( W n  | W 1 , …, W n-1 )
HMM s ,[object Object],[object Object],[object Object],S 0 S 1 S 2 S 3 a 00 a 11 a 22 b 0 (k) b 1 (k) b 2 (k)
Dictionary : ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],AM P ( A 1 , …, A T  | P 1 ,… , P k ) Dictionary P ( P 1 , P 2 , …, P k  | W ) LM P ( W n  | W 1 , …, W n-1 )
Language Model (LM): ,[object Object],[object Object],[object Object],AM P ( A 1 , …, A T  | P 1 ,… , P k ) Dictionary P ( P 1 , P 2 , …, P k  | W ) LM P ( W n  | W 1 , …, W n-1 )
RECOGNITION   ,[object Object],[object Object],[object Object],Dynamic Structure Search Algorithm S * Static Structure   S t  , P(x t ,{s t }| {s t-1 } ,  ) {S t-1 } x t
The Veterbi Beam search   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SIGN LANGUAGE   ,[object Object],[object Object]
AMERICAN SIGN LANGUAGE  ( ASL ) ,[object Object],[object Object],[object Object],[object Object]
ASL ALPHABETS ,[object Object],[object Object],[object Object],[object Object],[object Object],Aa Bb Cc Dd Ee Ff Gg Hh Ii Jj Kk Ll Mm Nn Oo Pp Qq Rr Ss Tt Uu Vv Ww Xx Yy Zz
SIGNED ENGLISH ( SE ): ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ASL  vs.  SE  (an Example) It is alright if you have a lot ASL  Translation SE  Translation IT I S ALL RIGHT IF YOU HAVE A LOT
DEMONSTRATION OF THE ASL IN OUR SW: A number of 2,600 ASL prerecorded video clips In case of nonbasic word, extract the basic word out of it Recognized Word  (SR engine’s output) Is the basic word within the ASL database vocabulary? The American  Manual Alphabet Only in case of a nonbasic input word,  append some suitable marker Final Output None of the database contents matched the input basic word No Yes Fingerspelling of the original input word The equivalent ASL video clip of the input word, some marker could be appended
Speech to Sign Language Interpreter System -  MILESTONE Thesis Writing Outline & Progress SW Development & Progress % Drafted Chapter 2:  State-of-the-Art of SR Chapter 3:  Sphinx SR Chapter 4:  Sphinx Decoder Chapter 5:  Sign Language Chapter 6:  SW Demo ., Conclusions  & Further Work Appendices SR Engine ASL Database Overall Integrated SW Chapter 1:  Introduction % Completed
Thank You ,[object Object],[object Object]

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Speech To Sign Language Interpreter System

  • 1. By: Khalid El-Darymli G0327887 Speech to Sign Language Interpreter System Supervisor: Dr. Othman O. Khalifa International Islamic University Malaysia Kulliyyah of Engineering, ECE Dept.
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  • 5. Main Parts of Speech to Sign Language Interpreter System Speech-Recognition Engine ASL pre-recorded Video-clips Database Recognized Text ASL Translation Continuous Input Speech Recognized Text
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  • 7. The Structure of SR Engine (LVCSR) Signal Processing AM P ( A 1 , …, A T | P 1 ,… , P k ) Dictionary P ( P 1 , P 2 , …, P k | W ) LM P ( W n | W 1 , …, W n-1 ) X={x 1 ,x 2 , …, x T } Hypothesis Evaluation Decoder P(X | W)*P(W) TRAINING DECODING Best Hypotheses H = {W 1 , W 2 , …, W k } W BEST Input Audio
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  • 18.
  • 19.
  • 20. ASL vs. SE (an Example) It is alright if you have a lot ASL Translation SE Translation IT I S ALL RIGHT IF YOU HAVE A LOT
  • 21. DEMONSTRATION OF THE ASL IN OUR SW: A number of 2,600 ASL prerecorded video clips In case of nonbasic word, extract the basic word out of it Recognized Word (SR engine’s output) Is the basic word within the ASL database vocabulary? The American Manual Alphabet Only in case of a nonbasic input word, append some suitable marker Final Output None of the database contents matched the input basic word No Yes Fingerspelling of the original input word The equivalent ASL video clip of the input word, some marker could be appended
  • 22. Speech to Sign Language Interpreter System - MILESTONE Thesis Writing Outline & Progress SW Development & Progress % Drafted Chapter 2: State-of-the-Art of SR Chapter 3: Sphinx SR Chapter 4: Sphinx Decoder Chapter 5: Sign Language Chapter 6: SW Demo ., Conclusions & Further Work Appendices SR Engine ASL Database Overall Integrated SW Chapter 1: Introduction % Completed
  • 23.