These slides are basically showing how to proceed in the area of Automatic Speech Recognition. I had implemented this whole project using simulation on JFLAP software.

2. We are considering that while giving speech to our
system. It is quite exhaustive that it has no noise
other than coming from user.
At certain places we use stored database in that
generates after training sets had done.
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3. To implement the above system we have 3
subsystems.
1. ASR (Automatic Speech Recognition)
2. DIALOGUE MANAGEMENT
3. SPOKEN LANGUAGE GENERATION
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4. This is the 1st subsystem used in SDS which takes
voice as input and converts it into grammatically
correct speech and stores in the system. This
system moreover focuses on making the voice
(including noise) into certain speech which further
can be used in our next subsystem. This is our main
area to focus.
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5. This system mainly focus in the management of the
output taken by ASR according to the individual
identity and Stores in the system for using in next
subsystem
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6. This subsystem uses stored speeches and generates
spoken language (say English in our case).
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7. 11/14/2012 7YoGiV

8. Now in our case we are dealing with
ASR (Automatic Speech Recognition)
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9. ASR will take voice as input and accordingly convert to
understandable speeches.
Question Arise
 How can system distinguish between different
speakers?
 How can system distinguish between ambient
noise and someone speaking?
 How can system derive meaning from what was
said?
 For the above questions we start to describe our
important part “Speech”
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10. Some of the factors which are to be taken in mind
while taking speech as input.
a) Biological Factors
b) Phonology
c) Frequency of Sounds
d) Timing
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11. 1. The way our mouth move to produce certain sounds
affect the features of the sound itself.
2. The structure of the mouth produces multiple
waves in certain patterns.
3. When we manipulate our mouths in the way to
make certain letters say‘t’ we push out more air at
once, making a higher frequency sound. So from
this we have one thing to take care is frequency of
speech and with frequency we take Amplitude and
Pitch into consideration.
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12.  It shows that how we use sound to convey meaning
in a language
 In English it states characteristics of sounds like
vowels and consonants.
 Phoneme is the smallest segmental unit of sound in
a language. Each Phoneme has features in the
sound that differs it from another Phoneme
Combine to represent words and sentences.
Regarding English we have about 40-50 phonemes.
So we use phoneme to remove any noise from the
sound
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13.  Different vowels have different pitches; they are
similar to musical notes
 for ex. 'i' being the highest 'u' being the lowest
 Consonant phonemes have more waves oscillating
of different parts of the mouth.
 So according to different frequency system we can
store words with different phoneme.
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14. There is a lot of information in timing. Breaks between
words, breaks between one sentence and another,
so this all to be considered in the speech to
distinguish between different words. According to
Research Vowels last longer than consonants.
 Now by looking above factors we have to:
 Translate from frequencies to a representation of a
phoneme.
 Discarding the useless information like noise, etc.
 The sentence created must make some sense.
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15. For the above problems we use two models and one
database:
 Acoustic Model
 Dictionary
 Language model
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16. Based on all the features of a sound wave
 Frequency
 Pitch
 Amplitude
 Time information
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17. ● The Acoustic Model is the statistical mapping from
the units of speech to all the features of speech.
● Convert Speech Sound to Phoneme then to Word
Statistical
● Tells information about the language Phonology.
It can learn from a training set.
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18. It checks the Word broken into the phoneme sounds
as what they are typically made of.
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19. ● Provides word-level structure for a language.
● Use formal grammar rules to make sentence. As we use context to place
particular word at particular place.
To implement the above context matching in systems we use technique of
Probability. For this we calculate probability of next coming word by
using previous probability
Probability of word is based on the last N-1 terms
P(Y) =∑ P (Y|X) P(X)
(Sum over x)
X= Probability of all the existing word in sentence.
Y= Probability of observing a sequence.
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20. 11/14/2012 20YoGiV

21. B. Tech , Computer Science
JIIT , Noida
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