It includes basic of Speech recognition system

1. Shree Manibhai Virani and Smt. Navalben Virani Science
College, Rajkot
(Autonomous)
Affiliated to Saurashtra University, Rajkot
Ms.Ripal Ranpara
Assistant Professor,
Department of Computer Science & Information Technology
Shree M.N. Virani Science College Rajkot

2. Monophones: It takes speech as input and divide speech into small segment,this
small segment are sound called monophones
.Grammer File : It contain grammer in form of rules. E.g English query.
.Voca File : It is used to define actual words in form of speech to text,it also contain
commanly used word.
.dict File & .dfa File: The .grammar file and .voca fi le are compiled to generate a
dictionary file and finite automata file, namely .dict and .dfa file, respectively.
These files are required at the time of execution of the system
.
HMM Creation Model:it is a statistical tool for modelling a wide range of time series
Data,it is mainly used as a part of speech tagging and noun phrase checking.
Julius Interface:its an interface to execute .dict file and .dfa file

3. Speaker dependent system :It is developed to operate for a single speaker.
These systems are usually easier to develop, cheaper to buy and more
accurate. Speaker–dependent software works by learning the unique
characteristics of a single person's voice, in a way similar to voice recognition.
Speaker independent system: It is developed to operate for any speaker of
a particular type (e.g. American English). Speaker–independent software is
designed to recognize anyone's voice, so no training is involved.
Speaker adaptive - A third variation of speaker models is now emerging,
called speaker adaptive. Speaker adaptive systems usually begin with a
speaker independent model and adjust these models more closely to each
individual during a brief training period.

4. Isolated word recognition - Isolated word recognizers usually require each
utterance to have quiet (lack of an audio signal) on BOTH sides of the sample
window. It doesn't mean that it accepts single words, but does require a single
utterance at a time.
Connected word recognition - Connect word systems (or more correctly
'connected utterances') are similar to Isolated words, but allow separate
utterances to be 'run−together' with a minimal pause between them.
Continuous speech recognition - Continuous recognition is the next step.
Recognizers with continuous speech capabilities are some of the most difficult
to create because they must utilize special methods to determine utterance
boundaries. Continuous speech recognizers allow users to speak almost
naturally, while the computer determines the content.

5. STEP 1: Basically, the microphone converts the voice to an analog signal. This
is processed by the sound card in the computer, which takes the signal to the
digital stage. This is the binary form of .1s. and .0s. that make up computer
programming languages.
STEP 2: Sound-recognition software has acoustic models ,An acoustic model is
created by taking audio recordings of speech, and their text transcriptions, and
using software to create statistical representations of the sounds that make up
each word. That each word is known as monophones
STEP 3: Once this is complete, a second sector of the software begins to
work. The language is compared to the digital dictionary that is stored in
computer memory. This is a large collection of words, usually more than
100,000. When it finds a match based on the digital form it displays the words
on the screen. This is the basic process for all speech recognition systems
and software.

7. Speech
Monophones
HMM
Creation
.dfa
.dict
Julius
Interface
Text As
Output

8. The conversion process of speech to text is divided into four phases, namely,
Data preparation,
Monophones HMM creation,
.grammer and .voca file creation,
Execution with Julius interface