Voice Controlled Robotic Vehicle By Ayesha Shafique. It is an end to end Robotic Vehicle Controlled through Voice.

1. Project Supervisor: Dr. Humera Tariq
Project Co - Supervisor: Sir Farid Alvi
Voice Controlled Robotic Car
By
Ayesha Shafique
University Of Karachi
Department Of Computer Science

2. 5/14/2018 BSCS-Final Year Project 2
“In a few years artificial intelligence virtual assistants will be as common
as the smart phone.” ~Steve jobs

3. 3
Problem Statement
Why It is important to Solve the
Chosen Problem?
Proper Block Diagram
GUI
intermediate and output images
(Important Results)
Preprocessing Details
Algorithm Description
Learning and Achievements
Improvements and Future Work
Demo and Queries
Contents

4. Problem Statement
The objective of the project is to allows
users to control the robotic vehicle
remotely by voice commands.
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Forward
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5. Importance
• People who historically have difficulties with
driving, such as disabled people and older
citizens, as well as the very young, would be
able to experience the freedom of car travel.
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6. DIAGRAMMATIC FLOW OF WORK
In this Section we described with the help of Diagrams, the principles we
studied and apply to input speech to address the mentioned problem.
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HIGH-LEVEL BLOCK DIAGRAM OF WORK
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COMPONENT DIAGRAM OF WORK
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ARCHITECTURE DIAGRAM
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10. GUI
In this section we show our GUI so that viewers can understand the
interaction in between input and output.
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Backward
Speech Recognizer
Backward
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12. RESULTS
Before getting into the theoretical/Mathematical details we will show
viewers some important results to grasp the techniques and concepts.
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INTERMEDIATE RESULTS
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Lx 293D Lx 293D
Door
motor
Turn
motor
Driver
motor
BREADBOARD CIRCUIT DESIGN IMPLEMENTATION
Power
Supply
Common
Ground
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FIRST WORKING PROTOTYPE OF ROBOTIC CAR
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Speech recognition Model’s Confusion Matrix
(in Isolated Environment)
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17. PREPROCESSING
In this Section we described the preprocessing applied on input speech
along with necessary code.
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CONVERSION TO WAV
File received from smart phone is of
‘.3gpp’,so to process we’ve to convert
it in ‘.wav’.
3gpp

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REMOVE SILENCE FROM WAV FILE
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GRAPHICAL REPRESENTATION
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21. ALGORITHM DESCRIPTION
Here we describes the key steps of algorithm that we used for Feature
Extraction and speech recognition
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SPEECH RECOGNITION SYSTEM COMPONENTS
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It convert a speech signal into a sequence of
acoustic feature vector to identify the components
of linguistic content and discarding all the other stuff
which carries information like background noise,
emotion etc.
MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC)
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HMM creates stochastic models from known utterances
and compares the probability that the unknown
utterance was generated by each model.
HIDDEN MARKOV MODEL (HMM)
HMM can be characterized by
following:
• Transition probability matrix
• Emission probability matrix
• Initial probability matrix
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Evaluation:
• Problem - Compute Probability of observation
sequence given a model
• Solution - Forward Algorithm and Viterbi Algorithm
Decoding:
• Problem - Find state sequence which maximizes
probability of observation sequence
• Solution - Viterbi Algorithm
Training:
• Problem - Adjust model parameters to maximize
probability of observed sequences
• Solution - Forward-Backward Algorithm
HMM PROBLEMS AND SOLUTIONS

26. LEARNING AND ACHIEVEMENTS
In this section we describe the things that makes us feel that we have
learned, we did something.
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LEARNING AND ACHIEVEMENTS
• Never ever bound your skill set to any
specific language
Select 1 or more language(s)
Map idea
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Cont.
• Signal Processing
Ready speech signal for processing
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Cont.
• Machine Learning Models
How machine learning models work?
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Cont.
• Arduino
How to control robots using Arduino?
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31. IMPROVEMENTS AND FUTURE
WORK
In this section we mention the tasks that are still unresolved. The
exceptional cases where still improvement is needed. We also mention
the applications that can build on top of our current work.
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• Need improvements in accuracy of speech
recognizer to make it works in noisy
environment.
• Use of sensors and radar to detect objects and
Cars’ surroundings as
Google’s self-driving car
project, relies heavily on LiDAR
and other sensors to
detect the presence of objects
in it’s vicinity.
IMPROVEMENTS
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FUTURE WORK
Same idea can be mapped for controlling a
wheelchair by means of human voice.
It enables a disabled person to move
around independently,
using a speech recognition
application which is
interfaced with motors.
Just we have to change
the hardware‘s framework.
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FINAL WORKING DEMO OF ROBOTIC CAR

35. Queries
For queries, please visit stall.
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36. Thank YouThank you
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