1. Lovely Professional University
Submitted By:
1. VIKRANT KHAJURIA ,
Project Guide: Dr. Vishal
Academic Year: 2022-2023
Department of Computer Application
Project Title: Project name
Lovely Professional University

2. Problem Statement
• Speech Recognition system which is working model can support Speech - to - Text & internally Text - to - Speech
mode itself.
• Project Code is purely based on Python.
• Speech Recognition system which I’ve made is GUI based which integrated with Python + Tkinter.
• Best part is: It is a hardware based on GUI based Python Designed Model.
• Live Execution will be shown here.

3. Literature Review Paper 01
Paper Title: Natural Language Processing Using Python
Author : Darvin Reynald J, Vismaya
• Natural Language Processing (NLP) is an area of application and research that explores how computers can be used to understand and
manipulate natural language speech or text to do useful things.
• The foundation of NLP lie in a number of disciplines, namely, computer and information sciences, linguistics, mathematics, electrical and
electronic engineering, artificial intelligence & robotics, and psychology.
• NLP researchers aim to gather knowledge on how human beings use and manipulate natural languages to perform desired tasks so that
appropriate tools and techniques can be developed.
• Applications of NLP include a number of fields of study e.g. multilingual and cross-language information retrieval (CLIR), machine
transaction, natural language, text processing and summarization, user interfaces, speech recognition, artificial intelligence and expert systems.
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4. 8CS7_0 Project, Session 2020-2021 4

5. Literature Review Paper 02
Paper Title: Speech Recognition by Machine, Author: M.A. Anusuya, S.K. Katti
• Speech Recognition (is also known as Automatic Speech Recognition (ASR), or computer speech recognition) is the process of converting a
speech signal to a sequence of words, by means of an algorithm implemented as a computer program.
• The main goal of speech recognition area is to develop techniques and systems for speech input to machine. Speech is the primary means of
communication between humans. For reasons ranging from technological curiosity about the mechanisms for mechanical realization of
human speech capabilities to desire to automate simple tasks which necessitates human machine interactions and research in automatic
speech recognition by machines has attracted a great deal of attention for 60 years.
• Based on major advances in statistical modeling of speech, automatic speech recognition systems today find widespread application in tasks
that require human machine interface, e.g. automatic call processing in telephone networks, and query based information systems that
provide updated travel information, stock price quotations, weather reports, Data entry, voice dictation, access to information: travel,
banking, Commands, Automobile portal, speech transcription, Handicapped people (blind people) supermarket, railway reservations etc.
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6. Literature Review Paper 02
• Speech recognition technology was increasingly used within telephone networks to automate as well as to enhance the operator services. This
report reviews major highlights during the last six decades in the research and development of automatic speech recognition, so as to provide
a technological perspective.
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Fig. - Basic Model of Speech Recognition

7. Literature Review Paper 03
Paper title : Speech Recognition System, Author: Shaikh Naziya S., R.R. Deshmukh
• Designing a machine that converse with human, particularly responding properly to spoken language has intrigued engineers and scientists for
centuries. Speech Recognition System(SRS) is also known as Automatic Speech Recognition (ASR) or computer speech recognition which is
the process of converting a speech signal to a sequence of words by means of an algorithm implemented as a computer program.
• It has the potential of being an important mode of interaction between humans and computers. Today speech technology enabled applications
are commercially available for a limited but interesting range of tasks. Very useful and valuable services are provided by these technology
enabled machines, by responding correctly and reliably to human voices.
• In order to bring us closer to the “Holy Grail” of machines that recognize and understand fluently spoken speech, many important scientific
and Technological advances have been took place, but still we are far from having a machine that mimics human behavior. Speech recognition
technology has become a topic of great interest to general population, through many block buster movies of 1960's and 1970's
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8. Literature Review Paper 03
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• Languages, on which so far automatic speech recognition systems have been developed, are just a fraction of the total around 7300
languages. Chinese, English, Russian, Portuguese, Vietnamese, Japan, Spanish, Filipino, Arabic, Bengali, Tamil, Malayalam, Sinhala and
Hindi are prominent among them.
Fig. - General steps for Speech Recognition System

9. Literature Review Paper 04
Paper Title: Speech Recognition: A Complete Perspective,Author: Ashok Kumar, Vikas Mittal 2019
• Speech recognition has gained a wide approval. This wide acknowledgement is resulted due to the revolution in storage
technology to handle big data like voice search on Google and other voice enabled interaction with mobile devices.
• Speech recognition has wide applications in many fields like voice user interface, domestic appliance control, voice
dialing, voice enabled search, data entry, learning applications for handicapped people.
• The performance of speech recognition system depends on its inertness to surrounding variabilities.
• There are many factors which are responsible for an effective recognition rate like environment conditions, speaker
dependent/ independent, rate of speech and channel variability. But for advanced speech recognition system it is required
to develop adaptive algorithm to match the change occurred and auto generative modeling capabilities to fill the gaps.
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10. Comparisons Table
S
No
Paper
Ref No
Paper
Title
Author’s
Name
Year Approach Used Gap Identified
1 [1] Natural
Language
Processing
Using Python
Darvin
Reynald J,
Vismaya
2017 This paper focuses on a simplified Natural Language
Processing (NLP) system using Python and Raspberry Pi. The
processes including voice extraction, speech to text
conversion, text processing have been explained along with
the python modules used to build the system.
Conversion process is classified into
two main sections:
-Speech to text recognition
-Text to speech conversion
2 [2] Speech
Recognition
by Machine
M.A.
Anusuya, S.K.
Katti
2019 The objective of this review paper is to summarize and
compare some of the well known methods used in various
stages of speech recognition system and identify research
topic and applications which are at the forefront of this
exciting and challenging field.
Speech Recognition system requires:
Definition of various types of speech
classes, speech representation, feature
extraction
techniques, speech classifiers,
database and performance
evaluation.

11. 3 [3] Speech
Recognition
System
Shaikh Naziya
S., R.R.
Deshmukh
2016 This paper gives an
overview of the speech recognition process,
its basic model, and its application,
approaches & also discuss comparative study
of different approaches which are used for
speech recognition system.
This paper also
provides an overview of different techniques
of speech recognition system & also shows
the summarization
some of the well-known methods used in
various stages of speech recognition system.
4 [4] Speech
Recognition: A
Complete
Perspective
Ashok Kumar,
Vikas Mittal
2019 The objective of this paper is to present a
complete perspective on speech recognition
describing various processes and
summarizing various methods used in a
typical speech system.
Important research challenges: speaker and
language variability, environmental noise and
the vocabulary size etc.
Comparisons Table

12. Conclusion
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• During this NSP Project we learnt to work directly on hardware through Python.
• Created GUI based Speech Recognition System.
• Used new concepts of Python, learnt and implemented them in project code.
• Got familiar with a demanding technical site, Speech Recognition.
• Further aim to enhance my knowledge more in this field and make my project more efficient to support other
language also.
• Google Translator is one of the motive to work and understand every concepts regarding this model.

13. Future Work & Discussion
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• Try to integrate with Speech Recognition APIs such as:
– Google Speech API
– IBM Watson API
– Speech API
– Speech to Text API
• We can use it with Live environment also where the system directly interact with User. Such as in ChatBot, Conversational
AI is best suited for this.
• Try to host on web portal so that it can be work as serverless program.
• We can also use this model in Android Mobile Application.

14. Reference
1. Natural Language Processing Using Python, International Journal of Scientific & Engineering Research, May-2017
2. Speech Recognition by Machine, International Journal of Computer Science and Information Security (IJCSIS), 2019
3. Speech Recognition System, IOSR Journal of Computer Engineering, Jul.-Aug. 2016
4. Speech Recognition: A Complete Perspective, International Journal of Recent Technology and Engineering (IJRTE), April 2019
5. IEEE POERTAL:- https://ieeexplore.ieee.org/abstract/document/7002390
6. RESEARCH GATE:- https://www.researchgate.net/publication/337155654_A_Study_on_Automatic_Speech_Recognition
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