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Page 1: silent sound technology

Silent Sound Technology

Najeeb P [EPAKECS029]

Guide : Raseek C , Asst.ProfessorDepartment of Computer Science and Engineering

Government Engineering College,Sreekrishnapuram

12th March 2014

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 2

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• Silence is the best answer for all the situations... even yourmobile understands!

• A technolgy that transforms the information without using ourvocal chords.

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 3

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 4

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Need For Silent Sound Technology

1

1http://www.go-gulf.com/crowd talking on mobile phones-300x225.jpgSilent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 5

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Need For Silent Sound Technology

• For a better way of communication in noisy public place.

• Perfect solution for those who lost their voice.

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 6

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 7

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Two methods are used

• Electromyography(EMG)

• Image Processing

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Electromyography(EMG)

2

2http://www.telecomspace.com/�les/cebit/device.jpgSilent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 9

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Electromyography(EMG)

• Even by saying words without producing sound weak electriccurrents are sent from brain to speech muscles. Thisphenomenon is called ELECTROMYOGRAPHS.

• A technique for evaluating and recording the electrical activityproduced by articulatory muscles.

• The transducers involved convert the pulses into electricalsignals.

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Electromyography(EMG)

3

3Reference[1]Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 11

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Working..

• The sensors(each with two �ne-wired electrodes) are attachedto the di�erent part of the face

• The electrical activity is recorded

• The sensors has to be placed at various locations to obtain anaccurate signal

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Working...

• Vocoder (voice encoder) converts this electrical signals intospeech patterns.

• Compare them with pre recorded speech pattern of spokenwords

• When there is a match, that sound is transmitted on to theother end of the line.

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Sensors used

• Pressure sensors

• Vibration sensors

• Electromagnetic sensor

• Motion sensors

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Image Processing

4

4http://www.telecomspace.com/silent sound/image processing.pngSilent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 15

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Image Processing

• Converting digital data tape into a �lm image with minimalcorrections and calibrations.

• In the silent sound technology the output of this imageprocessing is an audio record.

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 17

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Components

• Data acquisition

• Online recognition

• Sound playback or synthesis

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EMA based silent speech interface

5

5Reference [1]Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 19

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Data Collection and Processing[1]

Stimuli and Procedure

• A �xed number of sentences for basic greeting andconversation were selected form a list of most frequently usedsentences among AAC users

• The electromagnetic articulograph AG500 was used to registerthe 3-D movements of the tongue, lip, and jaw when a subjectwas talking.

• The articulograph records the movement by establishing acalibrated electromagnetic �eld.

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Data Collection and Processing[1]

Data Processing

• Head movements were subtracted from the tongue and liplocations.

• A zero-phase low-pass �lter was applied for removing signalnoise.

• All sequences were manually segmented and annotated withsentences.

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

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Features

• Native speakers can silently utter a sentence in their language,and the receivers can hear the translated sentence in theirlanguage.

• It appears as if the native speaker produced speech in a foreignlanguage.

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Features

• The translation technology works for languages like English,French and German.

• Allow people to make silent calls without bothering others.

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 25

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Restrictions

• This device presently needs nine leads to be attached to ourface which is quite impractical to make it usable.

• Complicated to identify person and emotions.

• Technology is di�cult to apply in some language like Chinese,where di�erent tones can hold many di�erent meanings.

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

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Applications

• Helping people who have lost their voice due to illness oraccident.

• Telling a trusted friend your PIN number over the phonewithout anyone eavesdropping � assuming no lip-readers arearound.

• Silent Sound Techniques is applied in Military forcommunicating secret/con�dential matters to others.

• E�ective calls in crowded place.

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Introduction

Why ..... ???

Methods

Design

Features

Restrictions

Applications

Conclusion

Silent Sound Technology Najeeb P [EPAKECS029] Department of Computer Science 29

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Conclusion

• Silent Sound Technology one of the recent trends in the �eld ofinformation technology implements �Talking without Talking�.

• It will be one of the innovation and useful technology and incoming future this technology will be use in our day to day life

• The online algorithm still has room for improvement.

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REFERENCE

• [1] Ashok samal,Frank Rudzicz,Jordan R Green and Jun Wang,�Sentence recognition from articulatory movements for SilentSpeech Interfaces� ,IEEE International Conference onAcoustics, Speech, and Signal Processing (ICASSP), 2012 ,Page(s): 4985 - 4988 .

• [2] Ashok samal,Jordan R Green, Jun Wang and Tom D.Carrell, �Vowel recognition from continuous articulatorymovements for speaker dependent application�, SignalProcessing and Communication Systems (ICSPCS), 2010 ,Page(s): 1-7.

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• [3] Guangqi Cao, Meng M. Helen and Zhiyong Wu,�A Uni�edFramework for Multilingual Text-to-Speech Synthesis withSSML Speci�cation as Interface�,IEEE Tsinghua Science andTechnology ,2009 , Page(s): 623 - 630 .

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Thank You for the attention

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