SPEECH-TO-TEXT-APP-CUSTOMIZED-FOR-POLICE-FUNCTIONING

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SPEECH-TO-TEXT-APP-CUSTOMIZED-FOR-POLICE-FUNCTIONING

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# Speech-to-text-app-customized-for-police-functioning

## ABSTRACT

Speech recognition technology is one from the fast growing engineering
technologies. It has a number of application in different areas and
provides potential benefits. Nearly 20% people of the world are suffering
from various disabilities; many of them are blind or unable to use their
hands effectively.
The speech recognition systems in those particular cases provide a
significant help to them, so that they can share information with people
by operating computer through voice input.
 This project is designed and developed keeping that factor into mind,
and a little effort to achieve this aim. Our project is capable to recognize
the speech and convert into text.

## Problem Statement

Speech recognition is the process that recognizes all words being
said by humans and to convert this speech into text and to
analyse this texxt to produce the results required by the humans.
The performance of this system majorly depends upon number of
factors such as the speed of the spoken words by the
user,vocabularies and the background noise caused by the
environment .The speech recognition library of the package
provided by the pypi library can be helpful in reducing various
factors such as background noise which then makes the speech
good for processing and the performing the tasks provided to this
system such as words recognition ,web searches .

## SPEECH RECOGNITION TYPES

### SPEECH RECOGNITION SYSTEM is basically Divided into following
### depending on various types:
#### Speaking Mode:
Basically it means that how the words are been spoken as in connected
or in isolated. In Isolated word of speech Recognition System needs that
speaker take pause between the words he speak. It means single kind
word In connected word of speech recognition system did not need that
the speaker take pause briefly in between the words. It generally means
full length sentences in which words are then artificially keep away by
silence.
#### Speaking Style:
Generally it Includes whether that the speech is in continuous form of
spontaneous form. Continuous form is that spoken in natural form.
Systems are to evaluated on speech read from the scripts that are
prepared where as in spontaneous or extemporaneously generated,
speech does not contain fluencies, and it is also difficult to figure out that
speech read from the written script. It is also vastly much more hard as it
tends to be peppered with unfluency like “uuh” and “uum”, no full
sentaces, spluttering , stuttering, sneezing , cough, and also vocabulary
is essentially ulimited, So there must be training to system to be able to
tackle with unknown and hidden words.
#### Vocabulary :
IT is much simple to discriminate a smaller set of the words, but rate of
error incareses as the size of the vocabulary increases.
For ex: 10 digits start from 0 -9 can easily be recognised rightly on the
other side vocabulary whose size is 100 , 4000 or 15000 have the rate of
error as 3%, 6%, 40% . The vocabulary is hard to predict or recognize if
it contains Confused kind of words.
#### Enrollment:
This is kind of 2 ways
1)Speaker Dependent 2) speaker independent
In speaker dependent the user must be providing various samples of her
or his speech before they’re used, a speaker dependent system is
meant for use of only single kind speaker , where as speaker
independent system is allowed or intended to use any type or kind of
speaker.

##  System Development Approach :

![](https://github.com/kvkkavin/Speech-to-text-app-customized-for-police-functioning/blob/main/Algorithm.png)<br>
![](https://github.com/kvkkavin/Speech-to-text-app-customized-for-police-functioning/blob/main/Different%20languages.png)<br>
![](https://github.com/kvkkavin/Speech-to-text-app-customized-for-police-functioning/blob/main/Speech-to-text-process-generic-model.png)<br>
![](https://github.com/kvkkavin/Speech-to-text-app-customized-for-police-functioning/blob/main/UserInterface.png?raw=true)<br>

## Conclusion:

The project of speech recognition gives us the introduction of this
technology and its various application in different sectors. The project is
divided into three parts ,the first which helps in converting audio to text
,the second which recognises the spoken word and the third which
performs the operations provided as the command by the user.After the
development of these parts these models were tested and the results
were produced which tells about the accuracy of each model.Various
advantages and disadvantages of this software is discussed.

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