Artificial Voice synthesizer using fuzzy logics

1
Artificial Voice Synthesizer
for Mute People
Guided by-
Prof. Kushal Birla
By-
Muskan Mansuri

2
Contents-
Artificial voice Synthesizer using Fuzzy Logic

Introduction
 In India around 70 million people are not able to speak.
 This device will help them communicate efficiently.
 It uses the concepts of Neural impulse sensing and
Artificial Intelligence.

Aim &
Objectives
▪ To make mute people communicate easily.
▪ To design easily wearable device.
▪ To design a user-friendly and affordable device.
4 Artificial voice Synthesizer using Fuzzy Logic

Methodology
▪ Methodology followed in the design:
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Extraction of
brain
impulses
Processing of
signals
Speech
Synthesis

Extraction of
Brain Impulses
▪ Every actions have specific brain impulses
▪ The neurons in brain produce electrical signals that form
brain waves.
▪ Brain waves categorized according to their frequencies.
▪ EEG method is used to extract brain waves.

Fig. Human Brainwaves

EEG Sensor
The Neural Sensor

EEG Sensor
▪ EEG stands for electroencephalogram.
▪ It is used to detect electrical activity in the brain.
▪ It filters the noise and separate different brainwaves.
▪ It is combined with the electrodes.

Electrodes
▪ Electrodes are biocompatible
▪ Used to extract brain’s
electrical signals.
▪ Fixed where more neurons are
present .

Fig: EEG sensor chip
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EEG Electrode
EEG Shield
Ground
Electrode
Reference
Shield
Reference
Electrode
VCC ‘+’
RXD &
TXD
output

Fig: The EEG headsets

Processing of
signals
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MSP430
Microcontroller
Analog to
Digital
converter
Wireless
transmitter
Signals
from EEG
sensor
Handheld
Device

Fuzzy Logics
The many valued logic
1
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Fuzzy Logic
▪ Fuzzy – “not clear, distinct, or precise; blurred”
▪ In which truth values of variables can be real no.
between 0 & 1.
▪ It is basically used for decision making.
▪ It resembles human reasoning.
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In this photo the sky is clear or cloudy?
Choose between 0 (clear sky) and 1 (cloudy sky).

Fuzzy Logic
▪ Value can be in a scale between 0 & 1.
▪ Representing the real situation by giving it a value.
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Clear sky Cloudy sky

Fuzzy Logic
Example:
• Sam is 28, 0.8 in set “YOUNG”
• Nick is 35, 0.1 in set “YOUNG”
• Ann is 23, 1.0 in set “YOUNG”
Unlike statics & probability, degree doesn’t specify
probabilities but instead describes to what extent it is in
the set.
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Fuzzy Logic
▪ Impulses are transformed using transformation function.
▪ Transformation functions- Discrete Fourier transform.
▪ Solutions are mapped with their respective words
▪ They are stored in database.
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Fuzzy Logic
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Signals obtained
using fuzzy logic
Stored fuzzy
logic
representations
Normal English
words to be
pronounced
0.25.0.45.0.4.0.1.2 0.33.0.46.0.90.990.6 WHAT
0.22.0.46.0.2.0.30.6 WHY
0.25.45.0.40.0.10.2 HOW

Speech
synthesis
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Digital to analog
converter
• Analog signals
to be given out
as voice
signals
Voice
transducer
• Device that
converts
energy from
one form to
another.

Fig: Digital to analog audio converter Adapter
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Fig: Visaton EX 60s piezo transducer (Voice transducer)
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Fig: Working of device
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Price: 1100/- INR Price: 1300/- INR Price: 300/- INR
Price: 250/- INR Price: 5700/- INR Price: 1200/- INR
Total estimated price: 10,000/- INR
1.Digital to Analog converter 2.Microcontroller 3.Electrodes
4.Wireless Transmitter 6.Analog to Digital converter5.EEG Sensor

Future scope
▪ Function and price of the device can be improvised.
▪ For voice synthesis, text to speech technique can be
used.
▪ Database updated with desired languages and new
gestures.
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Conclusion
▪ Communication is the essential part of day to day
lifestyle.
▪ “Artificial voice synthesizer” helps the dumb person in
overcoming their disability.
▪ It provides simple and efficient way to communicate.
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References
▪ Adiline Macriga G., Kruthiga K. and Anusudha S.,
"Artificial Voice synthesizer using fuzzy logics," 2017
2nd International Conference on Computing and
Communications Technologies (ICCCT), Chennai,
2017, pp. 388-392.
doi: 10.1109/ICCCT2.2017.7972313
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References
1. K. Rajeswari, E. Jeevitha and V. K. G. K. Selvi,
““Virtual Voice” — The voice for the dumb," 2010
IEEE International Conference on Computational
Intelligence and Computing Research, Coimbatore,
2010, pp. 1-3. (DOI: 10.1109/ICCIC.2010.5705909)
2. Davis, Gene & McConnell, Catherine & Popovic,
Djordje & Berka, Chris & Korszen, Stephanie. (2013).
Soft, Embeddable, Dry EEG Sensors for Real World
Applications. 10.1007/978-3-642-39454-6_28.
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References
3. D. Lee, H. Yoon and J. Kim, "Continuous gesture
recognition by using gesture spotting," 2016 16th
International Conference on Control, Automation and
Systems (ICCAS), Gyeongju,2016,pp.1496-1498.
DOI: 10.1109/ICCAS.2016.7832502
4. The ultimate guide to EEG sensor (web)-
http://neurosky.com/biosensors/eeg-sensor/ultimate-guide-
to-eeg/
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