1. Silent Sound Technology
Najeeb P [EPAKECS029]
Guide : Raseek C , Asst.Professor
Department of Computer Science and Engineering
Government Engineering College,Sreekrishnapuram
12th March 2014
3. • Silence is the best answer for all the situations... even your
mobile understands!
• A technolgy that transforms the information without using our
vocal chords.
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5. Need For Silent Sound Technology
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http://www.go-gulf.com/crowd talking on mobile phones-300x225.jpg
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6. Need For Silent Sound Technology
• For a better way of communication in noisy public place.
• Perfect solution for those who lost their voice.
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10. Electromyography(EMG)
• Even by saying words without producing sound weak electric
currents are sent from brain to speech muscles. This
phenomenon is called ELECTROMYOGRAPHS.
• A technique for evaluating and recording the electrical activity
produced by articulatory muscles.
• The transducers involved convert the pulses into electrical
signals.
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12. Working..
• The sensors(each with two ne-wired electrodes) are attached
to the dierent part of the face
• The electrical activity is recorded
• The sensors has to be placed at various locations to obtain an
accurate signal
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13. Working...
• Vocoder (voice encoder) converts this electrical signals into
speech patterns.
• Compare them with pre recorded speech pattern of spoken
words
• When there is a match, that sound is transmitted on to the
other end of the line.
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14. Sensors used
• Pressure sensors
• Vibration sensors
• Electromagnetic sensor
• Motion sensors
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16. Image Processing
• Converting digital data tape into a lm image with minimal
corrections and calibrations.
• In the silent sound technology the output of this image
processing is an audio record.
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18. Components
• Data acquisition
• Online recognition
• Sound playback or synthesis
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19. EMA based silent speech interface
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Reference [1]
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20. Data Collection and Processing[1]
Stimuli and Procedure
• A xed number of sentences for basic greeting and
conversation were selected form a list of most frequently used
sentences among AAC users
• The electromagnetic articulograph AG500 was used to register
the 3-D movements of the tongue, lip, and jaw when a subject
was talking.
• The articulograph records the movement by establishing a
calibrated electromagnetic eld.
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21. Data Collection and Processing[1]
Data Processing
• Head movements were subtracted from the tongue and lip
locations.
• A zero-phase low-pass lter was applied for removing signal
noise.
• All sequences were manually segmented and annotated with
sentences.
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23. Features
• Native speakers can silently utter a sentence in their language,
and the receivers can hear the translated sentence in their
language.
• It appears as if the native speaker produced speech in a foreign
language.
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24. 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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26. Restrictions
• This device presently needs nine leads to be attached to our
face which is quite impractical to make it usable.
• Complicated to identify person and emotions.
• Technology is dicult to apply in some language like Chinese,
where dierent tones can hold many dierent meanings.
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28. Applications
• Helping people who have lost their voice due to illness or
accident.
• Telling a trusted friend your PIN number over the phone
without anyone eavesdropping assuming no lip-readers are
around.
• Silent Sound Techniques is applied in Military for
communicating secret/condential matters to others.
• Eective calls in crowded place.
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30. Conclusion
• Silent Sound Technology one of the recent trends in the eld of
information technology implements Talking without Talking.
• It will be one of the innovation and useful technology and in
coming future this technology will be use in our day to day life
• The online algorithm still has room for improvement.
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31. REFERENCE
• [1] Ashok samal,Frank Rudzicz,Jordan R Green and Jun Wang,
Sentence recognition from articulatory movements for Silent
Speech Interfaces ,IEEE International Conference on
Acoustics, 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 articulatory
movements for speaker dependent application, Signal
Processing and Communication Systems (ICSPCS), 2010 ,
Page(s): 1-7.
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32. • [3] Guangqi Cao, Meng M. Helen and Zhiyong Wu,A Unied
Framework for Multilingual Text-to-Speech Synthesis with
SSML Specication as Interface,IEEE Tsinghua Science and
Technology ,2009 , Page(s): 623 - 630 .
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33. Thank You for the attention
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