This document is a project report submitted by three students - Ashwani Kumar, Ankit Raj, and Anand Abhishek - to Cochin University of Science & Technology in partial fulfillment of their Bachelor of Technology degree in Information Technology. The report describes a voice recognition mobile application called HandOVRS designed for physically handicapped users that can recognize common sounds in the home like doorbells, phones, and alarms and allow the user to select notification options like sending text messages.

1. HandOVRS
A PROJECT REPORT
submitted to
COCHIN UNIVERSITY OF SCIENCE & TECHNOLOGY
by
ASHWANI KUMAR (14140018)
ANKIT RAJ (14140014)
ANAND ABHISHEK (14140012)
in partial fulfillment for the award of the degree
of
BACHELOR OFTECHNOLOGY
in
INFORMATION TECHNOLOGY
DIVISION OF INFORMATION TECHNOLOGY
SCHOOL OF ENGINEERING
COCHIN UNIVERSITY OF SCIENCE & TECHNOLOGY
KOCHI- 682 022
KERALA | INDIA

2. HandOVRS
A PROJECT REPORT
submitted to
COCHIN UNIVERSITY OF SCIENCE & TECHNOLOGY
by
ASHWANI KUMAR (14140018)
ANKIT RAJ (14140014)
ANAND ABHISHEK (14140012)
in partial fulfillment for the award of the degree
of
BACHELOR OFTECHNOLOGY
in
INFORMATION TECHNOLOGY
DIVISION OF INFORMATION TECHNOLOGY
SCHOOL OF ENGINEERING
COCHIN UNIVERSITY OF SCIENCE & TECHNOLOGY
KOCHI- 682 022
KERALA | INDIA.

3. [i]
ACKNOWLEDGEMENT
We have taken a great effort in this project. However, it would not have been
possible without the kind support and help of many individuals who were directly or
indirectly involved. We would like to extend our sincere thanks to all of them.
We find ourselves grateful to Dr. Shelbi Joseph, Head Dept. of Information
Technology for his constant support and morale boosting. We are highly indebted to
Dr. Binsu C. Kovoor, Assistant Professor, Dept. of Information Technology for her
guidance and constant supervision as well as for providing necessary information.
We would like to express our special gratitude and thanks to Mrs. Sariga Raj,
Assistant Professor Dept. of Information Technology and project guide as she was
always there to help us out in project preparation and completion.
Our thanks and appreciations also goes to our colleague in developing the project and
people who have willingly helped us out with their abilities.
We would like to express our gratitude towards our parents for their kind co-
operation and encouragement which helped us in completion of this report.

4. [ii]
DIVISION OF INFORMATION TECHNOLOGY
SCHOOL OF ENGINEERING
COCHIN UNIVERSITY OF SCIENCE & TECHNOLOGY
CERTIFICATE
This is to certify that the project report entitled “HandOVRS” submitted
by ASHWANI KUMAR, ANKIT RAJ and ANAND ABHISHEK to
the Cochin University of Science & Technology, Kochi, Kerala in partial
fulfillment for the award of Degree of Bachelor of Technology in
Information Technology is a bonafide record of the project work carried
out by him under my supervision during JULY 2016- DECEMBER
2016.
Dr. Shelbi Joseph Sariga Raj
Head (Project Guide)
Department of Information Technology Assistant Professor

5. [iii]
ABSTRACT
Voice Recognition Service for handicapped (HandOVRS) is a mobile application capable of
recognition some of voices. And due to the system compatibility, users can run this
application from any mobile devices supported by android system and HandOVRS is
designed especially for physically handicapped people. Voice recognition service (VRS)
recognize voices in our real life especially in our home such that sound of the bell, telephone
and door. HandOVRS allows the user to many of the notifications options when you hear a
specific sound previously identified where the user can choose one of the available options
such as sending a message to mobile number or vibration. HandOVRS requires minimum
knowledge of how to use the mobile in order to be able to run it. HandOVRS has English,
simple and user-friendly interface. This document includes a detailed description of system
requirements both functional and non-functional, design models and description,
functionalities of all system objects and future scope so it could be used as a user manual for
system users.
Keywords: Voice Recognition Service for handicapped (HandOVRS); Voice Recognition
Service (VRS); android (Mobile OS).
.

6. [iv]
CONTENTS
S.no.
Title
Page number
Title Page
Acknowledgement
Certificate
Abstract
Table of Contents
List of Tables
List of Figures
List of Abbreviations
i
ii
iii
iv
v
vii
1. INTRODUCTION
1.1 WHY VOICE RECOGNITION SERVICE (VRS)? 1
1.2 PROJE C T DESCRIPTION 1
1.3 SİM İLA R PREVIOUS SYSTEMS 2
2. VOICE RECOGNITION BACKGROUND
2.1 WHAT IS VOICE RECOGNITION? 3
2.2 HOW IS VOICE RECOGNITION PERFORMED? 4
2.3 HOW IT WORKS? 6
2.4 STRENGTHS AND WEAKNESSES 7

7. [v]
2.5 WHAT DISCIPLINES ARE INVOLVED IN VOICE
RECOGNITION
8
2.6 ENRO LM E N T 9
2.7 VOIC E RECOGNITION APPLICATIONS 9
2.8 PAT T ERN MATCHING
10
3. SYSTEM REQUIREMENTS
3.1 FUNCTIONAL REQUIREMENTS 11
3.2 NON-FUNCTIONAL REQUIREMENTS 14
4. SYSTEM DESIGN
4.1 USE CASE MODEL 15
4.1.1 ACTORS 16
4.1.2 USE CASES 16
4.1.3 SEQUENCE DIAGRAM 17
4.1.4 SYSTEM ARCHITECTURE DIAGRAM 17
5. SOFTWARE SPECIFCATIONS
5.1 PROGRAM 18
5.2 SOFTWARE REQUIREMENTS 24
6. TESTING AND IMPLEMENTATIONS
6.1 IMPLEMENTATION ISSUES 25

8. [vi]
6.2 TESTING 26
7. USED SOFTWARE TOOLS 28
6.1 ANDROID STUDIO
8. CONCLUSION AND FUTURE ENHANCEMENTS 30
9. REFERENCES 31

9. [vi]
LIST OF FIGURES
TITLE PAGE No.
Whistle Android Finder Free
Use Case Diagram
Sequence Diagram
Android Architecture Diagram
Start Page View
Main Activity View
Speak Something Activity
Listening Voice Activity
Speech to Text Output View
2
15
16
17
26
27
27
28
28

10. [vii]
LIST OF ABBREVIATIONS
ABBREVIATIONS EXPANSION
HandOVRS Voice Recognition Service for Handicapped
VRS Voice recognition service
A/D Analog-to-digital
LPC Linear predictive coding
ADT Android Development Tools
IDE Integrated development environment
-

11. 1. Introduction
1.1 WHY VOICE RECOGNITION SERVICE (VRS)?
There is a slice of people in our society are neglected and unable to communicate
with ordinary people because of their disability, they are handicapped. A physically
handicapped person cannot live in the house alone, without another person guide
him on the strange events to get inside the house for example (ring a bell house or
ring phone home or fire alarms).
We decided to employ the best of our knowledge at the service of this slide
neglected programming application can run on smart handheld devices and thus feel
the physically handicapped people that disability does not prevent him from
communicating with the outside world.
This program is not limited to use only on physically handicapped person, but the
ordinary person can use it for other purposes for example, when a fire was
handicapped person is attentive will be sent an SMS to Mobile someone else inform
him of the existence of a fire inside the house.
1.2 PROJECT DESCRIPTION
HandOVRS is an application that runs on all devices that use Android system. The
project consists of 3 interfaces, one major and two subcommittees the main,
interface contain several options the user can control it, and interface appear to
start listening to the voices and the interface showing the result.
Interfaces convenient and easy to use. In the main interface the user can choose that
the alarm is received through email or by sending a text message to a phone number
to be determined in advance and can choose a vibration when a voice
1.3 SIMILAR PREVIOUS SYSTEMS
i. Whistle Android Finder FREE

12. - 2 -
The Whistle Android Finder FREE Figure 1.1 is amazing Android finder (phone
locator) application which has helped many people locate their Android device with
just a simple WHISTLE after losing it, forgetting it somewhere or being hidden
somewhere in a room on a desk behind papers in a brief case or in a coat pocket.

13. - 3 -
2. VOICE RECOGNITION BACKGROUND
Voice recognition is the process of taking the spoken word as an input to a computer
program. This process is important to virtual reality because it provides a fairly
natural and intuitive way of controlling the simulation while allowing the user's
hands to remain free. This article will delve into the uses of voice recognition in the
field of virtual reality, examine how voice recognition is accomplished, and list the
academic disciplines that are central to the understanding and advancement of voice
recognition technology.
2.1 WHAT IS VOICE RECOGNITION, AND WHY IS IT USEFUL
IN A VIRTUAL ENVIRONMENT?
Voice recognition is "the technology by which sounds, words or phrases spoken by
humans are converted into electrical signals, and these signals are transformed into
coding patterns to which meaning has been assigned" . While the concept could
more generally be called "sound recognition", we focus here on the human voice
because we most often and most naturally use our voices to communicate our ideas
to others in our immediate surroundings. In the context of a virtual environment, the
user would presumably gain the greatest feeling of immersion, or being part of the
simulation, if they could use their most common form of communication, the voice.
The difficulty in using voice as an input to a computer simulation lies in the
fundamental differences between human speech and the more traditional forms
of computer input. While computer programs are commonly designed to produce a
precise and well-defined response upon receiving the proper (and equally precise)
input, the human voice and spoken words are anything but precise. Each human
voice is different, and identical words can have different meanings if spoken with
different inflections or in different contexts. Several approaches have been tried,
with varying degrees of success, to overcome these difficulties.

14. - 4 -
2.2 HOW IS VOICE RECOGNITION PERFORMED?
The most common approaches to voice recognition can be divided into two classes:
"template matching" and "feature analysis". Template matching is the simplest
technique and has the highest accuracy when used properly, but it also suffers from
the most limitations. As with any approach to voice recognition, the first step is for
the user to speak a word or phrase into a microphone. The electrical signal from the
microphone is digitized by an "analog-to-digital (A/D) converter", and is stored in
memory. To determine the "meaning" of this voice input, the computer attempts to
match the input with a digitized voice sample, or template, that has a known
meaning. This technique is a close analogy to the traditional command inputs from a
keyboard. The program contains the input template, and attempts to match this
template with the actual input using a simple conditional statement.
Since each person's voice is different, the program cannot possibly contain a
template for each potential user, so the program must first be "trained" with a new
user's voice input before that user's voice can be recognized by the program. During
a training session, the program displays a printed word or phrase, and the user
speaks that word or phrase several times into a microphone. The program computes
a statistical average of the multiple samples of the same word and stores the
averaged sample as a template in a program data structure. With this approach to
voice recognition, the program has a "vocabulary" that is limited to the words or
phrases used in the training session, and its user base is also limited to those users
who have trained the program. This type of system is known as "speaker
dependent." It can have vocabularies on the order of a few hundred words and short
phrases, and recognition accuracy can be about 98 percent.
A more general form of voice recognition is available through feature analysis
and this technique usually leads to "speaker-independent" voice recognition. Instead

15. - 5 -
of trying to find an exact or near-exact match between the actual voice input and a
previously stored voice template, this method first processes the voice input using
"Fourier transforms" or "linear predictive coding (LPC)", then attempts to find
characteristic similarities between the expected inputs and the actual digitized voice
input. These similarities will be present for a wide range of speakers, and so the
system need not be trained by each new user. The types of speech differences that
the speaker-independent method can deal with, but which pattern matching would
fail to handle, include accents, and varying speed of delivery, pitch, volume, and
inflection. Speaker-independent speech recognition has proven to be very difficult,
with some of the greatest hurdles being the variety of accents and inflections used by
speakers of different nationalities. Recognition accuracy for speaker-independent
systems is somewhat less than for speaker-dependent systems, usually between 90
and 95 percent.

16. - 6 -
2.3 HOW IT WORKS?
Voice recognition technology utilizes the distinctive aspects of the voice to
verify the identity of individuals. Voice recognition is occasionally confused with
speech recognition, a technology which translates what a user is saying (a process
unrelated to authentication). Voice recognition technology, by contrast, verifies the
identity of the individual who is speaking. The two technologies are often bundled
– speech recognition is used to translate the spoken word into an account number,
and voice recognition verifies the vocal characteristics against those associated with
this account.
Voice recognition can utilize any audio capture device, including mobile and
land telephones and PC microphones. The performance of voice recognition
systems can vary according to the quality of the audio signal as well as variation
between enrollment and verification devices, so acquisition normally takes place
on a device likely to be used for future verification.
During enrollment an individual is prompted to select a passphrase or to repeat
a sequence of numbers. The passphrases selected should be approximately 1-1.5
seconds in length – very short passphrases lack enough identifying data, and long
passwords have too much, both resulting in reduced accuracy. The individual is
generally prompted to repeat the passphrase or number set a handful of times,
making the enrollment process somewhat longer than most other biometrics.

17. - 7 -
2.4 STRENGTHS AND WEAKNESSES:
One of the challenges facing large-scale implementations of biometrics is the need
to deploy new hardware to employees, customers and users. One strength of
telephony-based voice recognition implementations is that they are able to
circumvent this problem, especially when they are implemented in call center and
account access applications. Without additional hardware at the user end, voice
recognition systems can be installed as a subroutine through which calls are routed
before access to sensitive information is granted. The ability to use existing
telephones means that voice recognition vendors have hundreds of millions of
authentication devices available for transactional usage today.
Similarly, voice recognition is able to leverage existing account access and
authentication processes, eliminating the need to introduce unwieldy or confusing
authentication scenarios. Automated telephone systems utilizing speech recognition
are currently ubiquitous due to the savings possible by reducing the amount of
employees necessary to operate call centers. Voice recognition and speech
recognition can function simultaneously using the same utterance, allowing the
technologies to blend seamlessly. Voice recognition can function as a reliable
authentication mechanism for automated telephone systems, adding security to
automated telephone-based transactions in areas such as financial services and
health care.
Though inconsistent with many users’ perceptions, certain voice recognition
technologies are highly resistant to imposter attacks, even more so than some
fingerprint systems. While false non-matching can be a common problem, this
resistance to false matching means that voice recognition can be used to
protect reasonably high-value transactions.
Since the technology has not been traditionally used in law enforcement or
tracking applications where it could be viewed as a Big Brother technology, there is

18. - 8 -
less public fear that voice recognition data can be tracked across databases or used
to monitor individual behavior. Thus, voice recognition largely avoids one of the
largest hurdles facing other biometric technologies, that of perceived invasiveness.
2.5 WHAT D I S C I PLI N ES ARE INVOLVED IN V O I C E
RECOGNITION?
The template matching method of voice recognition is founded in the general
principles of digital electronics and basic computer programming. To fully
understand the challenges of efficient speaker- independent voice recognition, the
fields of phonetics, linguistics, and digital signal processing should also be explored.
2.6 ENROLMENT
Everybody’s voice sounds slightly different, so the first step in using a voice-
recognition system involves reading an article displayed on the screen. This process,
called enrolment, takes less than 10 minutes and results in a set of files being created
which tell the software how you speak. Many of the newer voice-recognition
programs say this is not required, however it is still worth doing to get the best
results. The enrolment only has to be done once, after which the software can be
started as needed.
2.7 VOICE RECOGNITION APPLICATIONS:
Voice recognition is a strong solution for implementations in which vocal
interaction is already present. It is not a strong solution when speech is introduced
as a new process. Telephony is the primary growth area for voice recognition, and
will likely be by far the most common area of implementation for the technology.
Telephony-based applications for voice recognition include account access for
financial services, customer authentication for service calls, and challenge-response
implementations for house arrest and probation- related authentication. These
solutions route callers through enrollment and verification subroutines, using
vendor-specific hardware and software integrated with an institution's existing

19. - 9 -
infrastructure. Voice recognition has also been implemented in physical access
solutions for border crossing, although this is not the technology's ideal deployment
environment.
2.8 PATTERN MATCHING:
The pattern matching process involves the comparison of a given set of input
feature vectors against the speaker model for the claimed identity and computing
a matching score. For the Hidden Markov models discussed above, the
matching score is the probability that a given set of feature vectors was
generated by the model.

20. - 10 -
3. REQUIREMETS AND SPECIFICATIONS
3.1 FUNCTIONAL REQUIREMENTS
3.1.1 UserFunctionalities
User is considered the person with ultimate authorities in the system with many
functionalities. First, admin can change the program's main settings including
program name, select voice type, select the shape of result and choose the tone
with result. Furthermore, user can modify the message content which contain the
result and determine if he want to send the result on another destination as another
telephone or email. The user can specify the type of the resulting tone and length,
for example can be to choose a bell tone if the result sound is the sound of the bell.
Also can select the type of output (just tone, just vibration or tone and vibration).
Can also select some or all of the voices from the list to be recognized by the
program. This helps to increase the speed of the program if a few selected voices.
Managing program is another critical issue for an user to handle. User is
responsible for adding, deleting, and editing Voice information. On the other hand
any user cannot manipulate neither user nor each other information.

21. - 11 -
3.2 NON-FUNCTIONALREQUIREMENTS
Performance Requirements
System should recognize to any Voices in his list without any fault. With Ideal
conditions, system response should be fast and error-free. System performance
shall not decrease with time or by usage. Performance and speed should not
depend on newer or older mobile, on mobile phone capable of running Android
programs or not.
Security Requirements
Change the data is only allowed to users and forbidden to any user. Program run
without web, that is mean Protected from hackers.
Software QualityAttributes
Availability
This System will be up to date and offer all the facilities to the users. Also
view the right detection.
Flexibility
This System will be easy to learn and easy to use, also will be provide help
page for all new users. Meaningful notification messages are displayed when
some error happens.
Robustness
No wrong information will be entered.
Accuracy
The system will not permit to the users enter any invalid data. Every user must
have a unique identification.

22. - 12 -
Adaptability
HandOVRS supports adding new voices without changing old voices. The
software is flexible and scalable so it can fit any Voice.
Aesthetics
The finished software has a nice view and appropriate to the nature of its work as
a management program. All text will be clear and well readable.
Compatibility
All users using this program will interact with any voice without any changes to
the original code. The system will be compatible with all mobile capable to
run android program.
Frequency/Severity of Failure
There will not be any unhandled exceptions from incorrect user input.
Human Factors
GUI is user-friendly and attractive. Menus have a consistent format. There will
be a lot of expressive images.
Maintainability
Software development team will be the maintenance team for any error or
defect.
Predictability
The system can never crash. The system must produce predictable results.
Reliability
The system will be available 100% of the time.

23. - 13 -
Response Time
Query response time must be fast. All queries must return a response in < 2
seconds.
Understandability
All users can learn to operate major use cases without outside assistance.
Testability
All major use cases must be tested.100% of the quality requirements must be
measurable.
.

24. - 14 -
4. SYSTEM DESIGN
This chapter describes the main aspects of the system design and architecture. The first
section describes the design represented in terms of use case diagrams. The second
section provides class diagrams that were designed for HandOVRS system. And
finally the third section provides brief information about modules of the system.
4.1 USE CASE MODEL
4.1.1 Actors:
There are one type of actors in the system namely administrator. The actors have access
of the system without authorization.
4.1.2 Use Cases:
The Use Case diagram for the system is shown in Figure 4.1. As can be seen from
the diagram the actor has access to different Use Cases. The administrator is able to
manage all resources; it is only the user who can add modify and general settings and
manage librarians. The Use Case diagram for the system is shown in Figure 4.1. As
can be seen from the diagram user has access to different Use Cases.

25. - 15 -
UseCase Diagram 1
The user is able to manage all resources (change time vibration, change output state (only
vibrate, only song, vibrate and song), send the output to email or mobile SMS or together
both email and SMS mobile).

26. - 16 -
4.1.3 Sequence diagram:

27. - 17 -
4.1.4 System Architecture diagram:

28. - 18 -
5.SOFTWARE SPECIFICATIONS
Android SDK Software (IDE) is used for writing programs for Android. It’s provided by
google as meant specially for Android. We have used this IDE for most of the developing
and testing purpose.
5.1 Program
The following codes are used for the developing our project. The main modules are as
follows :
5.1.1 MainActivity
package com.example.saswat.testingproject;
import android.content.Intent;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.view.Menu;
import android.view.View;
import android.widget.Button;
import static android.R.attr.button;
public class MainActivity extends AppCompatActivity {
private Button startBtn;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
//find the Button First
startBtn = (Button) findViewById(R.id.StartButton);
//create the OnClick Listener
startBtn.setOnClickListener(new
View.OnClickListener() {
@Override
public void onClick(View v) {
//here start the new Activity here..
Intent startMyIntent = new
Intent(MainActivity.this,SecondActivity.class);
startActivity(startMyIntent);
}
});
}

29. - 19 -
public boolean OnCreateOptionsMenu(Menu menu){
getMenuInflater().inflate(R.menu.activity_main,menu);
return true;
}
}
5.1.2 SecondActivity
package com.example.saswat.testingproject;
import android.content.ActivityNotFoundException;
import android.content.Intent;
import android.speech.RecognizerIntent;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.view.View;
import android.widget.ImageButton;
import android.widget.TextView;
import android.widget.Toast;
import java.util.ArrayList;
import java.util.Locale;
public class SecondActivity extends AppCompatActivity {
private final int SPEECH_RECOGNITION_CODE =1;
private TextView txtOutput;
private ImageButton btnMicrophone;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_second);
txtOutput = (TextView)
findViewById(R.id.txt_output);
btnMicrophone = (ImageButton)
findViewById(R.id.btn_mic);
btnMicrophone.setOnClickListener(new
View.OnClickListener() {

30. - 20 -
@Override
public void onClick(View v) {
startSpeechToText();
}
});
}
/**
* Start speech to text intent. This opens up Google
Speech Recognition API dialog box to listen the speech
input.
* */
private void startSpeechToText() {
Intent intent = new
Intent(RecognizerIntent.ACTION_RECOGNIZE_SPEECH);
intent.putExtra(RecognizerIntent.EXTRA_LANGUAGE,
Locale.getDefault());
intent.putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL,
RecognizerIntent.LANGUAGE_MODEL_FREE_FORM);
intent.putExtra(RecognizerIntent.EXTRA_PROMPT,
"Speak something...");
try {
startActivityForResult(intent,
SPEECH_RECOGNITION_CODE);
} catch (ActivityNotFoundException a) {
Toast.makeText(getApplicationContext(),
"Sorry! Speech recognition is not
supported in this device.",
Toast.LENGTH_SHORT).show();
}
}
/**
* Callback for speech recognition activity
* */
@Override
protected void onActivityResult(int requestCode, int
resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode,
data);
switch (requestCode) {
case SPEECH_RECOGNITION_CODE: {
if (resultCode == RESULT_OK && null != data)

31. - 21 -
{
ArrayList<String> result = data
.getStringArrayListExtra(RecognizerIntent.EXTRA_RESULTS);
String text = result.get(0);
txtOutput.setText(text);
}
break;
}
}
}
}
5.1.3ActivityMain XML
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout
xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools"
android:id="@+id/activity_main"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:paddingBottom="@dimen/activity_vertical_margin"
android:paddingLeft="@dimen/activity_horizontal_margin"
android:paddingRight="@dimen/activity_horizontal_margin"
android:paddingTop="@dimen/activity_vertical_margin"
tools:context="com.example.saswat.testingproject.MainActivit
y">
<Button
android:text="Start Now!"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:id="@+id/StartButton"
style="@style/Widget.AppCompat.Button.Colored"
android:visibility="visible"
android:elevation="10dp"
android:layout_alignParentBottom="true"
android:layout_centerHorizontal="true"
android:layout_marginBottom="132dp" />
<TextView

32. - 22 -
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:text="Welcome to HandOVR!"
android:textAppearance="@style/TextAppearance.AppCompat.Titl
e"
android:layout_above="@+id/StartButton"
android:layout_alignParentRight="true"
android:layout_alignParentEnd="true"
android:layout_marginRight="58dp"
android:layout_marginEnd="58dp"
android:layout_marginBottom="56dp" />
</RelativeLayout>
5.1.4 SecondAcitvity XML
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout
xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools"
android:id="@+id/activity_second"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:background="@color/bg_color"
android:orientation="vertical"
tools:context="com.example.saswat.testingproject.SecondActiv
ity">
<TextView
android:id="@+id/txt_output"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_alignParentTop="true"
android:layout_centerHorizontal="true"
android:layout_marginTop="100dp"
android:textColor="@color/colorAccent"
android:textSize="26dp"
android:textStyle="bold"/>
<LinearLayout
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginBottom="66dp"
android:gravity="center"

33. - 23 -
android:orientation="vertical"
android:layout_alignParentBottom="true"
android:layout_centerHorizontal="true">
<ImageButton
android:id="@+id/btn_mic"
android:layout_width="80dp"
android:layout_height="80dp"
android:background="@null"
android:scaleType="centerCrop"
android:src="@drawable/microphone" />
<TextView
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginTop="10dp"
android:text="Speech to text using Google API"
android:textColor="@color/white"
android:textSize="15dp"
android:textStyle="normal" />
</LinearLayout>
</RelativeLayout>
5.1.5 Strings XML
<resources>
<string name="app_name">TestingProject</string>
<string name="startBtn">Start Now!</string>
<string name="txt_output">TextOutput</string>
<string name="speech_prompt">Say
something&#8230;</string>
<string name="speech_not_supported">Sorry! Your device
doesn't support speech input</string>
<string name="tap_on_mic">Tap on mic to speak</string>
</resources>

34. - 24 -
5.2 SYSTEM REQUIREMENT
Windows OS X/ mac OS LINUX
OS version Microsoft Window
10/8/7(32 or 64-bit)
Mac OS X 10.9.5 or
higher, up to 10.11.6
(El Capitan) or
10.12.1 (Sierra)
GNOME or KDE
desktop
RAM 2 GB RAM minimum, 8 GB RAM recommended
Disk Space 500 MB disk space for Android Studio, at least 1.5 GB for Android SDK,
emulator system images, and caches
Java Version Java Development Kit (JDK) 8
Screen Resolution 1280x800 minimum screen resolution

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6.TESTING & IMPLEMENTATIONS
6.1 IMPLEMENTATION ISSUES
6.1.1 Recognizing the Speech
This is the main problem in my project, we tried more time to solve this problem
and we got success, at the beginning of the project, the program recognize only one
voice (speech) but when we want to add new voice to recognize it, we face many
problems like overlap between two signals and two voice appeared and the
recognition was bad and when we added another voice (Third voice) the process
became more complicated but we solved this problem we depend on features of
signal to distinguish signals so distinguish feature from signal to another, we chose
ranges different from other ranges to prevent overlapping signals.
6.1.2 Word Error Rate(WER)
Accuracy of Recognition: Accuracy is measured with the Word Error Rate
(WER), whereas speed is measured with the real-time factor.
WER can be computed by the equation,
WER = (S+D+I) / N
Where S is the number of substitutions, D is the number of the deletions, I is the
number of the insertions and N is the number of words in the reference.

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6.2 TESTING
6.2.1 MainUserInterface(MainView)
The main interface for my program it includes many features to facilitate for users.
Features like:
 Start Button where we need to click to enter the HandOVRS. There is also
a welcome message displayed here for the user.
Start View of the HandOVRS 1

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 Main Activity where we come for the voice recognition and testing we
need to tap on the mic to speak something.
Main Activity
 Tap on the mic to use the speech (voice ) to text as provided in the app.
Speak something

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 Afterthe Google APIdetectsthe speech itgivesoutthe resultasa form of text.
Text Result

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7. USED SOFTWARE TOOLS
7.1 ANDROIDSTUDIO
Android Studio is the official integrated development environment (IDE)
for Android platform development. It was announced on May 16, 2013 at the Google
I/O conference. Android Studio is freely available under the Apache License 2.0.Android
Studio was in early access preview stage starting from version 0.1 in May 2013, then
entered beta stage starting from version 2.2 which was released in June 2016. The first
stable build was released in December 2014, starting from version 1.0.
Based on Jet Brains' IntelliJ IDEA software, Android Studio is designed specifically for
Android development. It is available for download on Windows, Mac OS
X and Linux, and replaced Eclipse Android Development Tools (ADT) as Google's
primary IDE for native Android application development.
7.2 FEATURES
New features are expected to be rolled out with each release of Android Studio. The
following features are provided in the current stable version.
 Gradle-based build support.
 Android-specific refactoring and quick fixes.
 Lint tools to catch performance, usability, version compatibility and other problems.
 ProGuard integration and app-signing capabilities.
 Template-based wizards to create common Android designs and components.
 A rich layout editor that allows users to drag-and-drop UI components, option
to preview layouts on multiple screen configurations.
 Support for building Android Wear apps
 Built-in support for Google Cloud Platform, enabling integration with Google Cloud
Messaging and App Engine.

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8. CONCLUSION & FUTURE WORK
8.1 SUMMARY
HandOVRS which capable of detect voice and recognize it to know sound’s enables user
some option to get alarm when voice detect, it can send email with alarm type or by SMS to
mobile number. HandOVRS enables user to choose song and run it when voice is
detected and system can choose song automatically. HandOVRS enables user to choose
how much second mobile will vibrate when sound detected.
Finally, H a n d O VRS is flexible, effective, and attractive system. This
system can be easily extended by introducing new modules and implementing new
interfaces.
8.2 FUTURE WORK
There is no limit for development, improving and evaluating. Our future
vision is to implement and extend this system to support multiple languages rather than
English. We also hope to add some services not included in this version of the system such
as Prayer Time service which can be used to send notifications to user on their mobiles or
email or run some voice when Prayer Time is coming.
8.3 RECOMMENDATION
With a system like HandOVRS depth users can easily and attractively deal with
different sound If all depth people in our country started to use HandOVRS they will
Interacts with real sound and feel different and good .Every person can help with this
improvement according to his work field. Our minds should not stop on some fields for
development. Our field for development is the electronic management using the newest
technologies. We should do the best every time for better life and future.

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9. References
[1.] B. Raghavendhar Reddy, E. Mahender : Speech to Text
Conversion using Android Plat-form, International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 Vol. 3, Issue 1, January -February
2013, pp.253-258.
[2.] “Android Developers Google Website”; https://developer.android.com
[3.] Ms. Anuja Jadhav,Prof. Arvind Patil : Android Speech to Text Converter for SMS
Application, IOSR Journal of Engineering Mar. 2012, Vol. 2(3) pp: 420-423
[4.] Wikipedia Website