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Apurva Mittal (20141009) Ketan Gyanchandani (20141028) Riya Giri (20141058) Sanjeev Kumar (20141063) Saurabh Ojha (20141064) Vikash Kumar (20141072) Group 10

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Page 1: Machine learning

Apurva Mittal (20141009)Ketan Gyanchandani (20141028)Riya Giri (20141058)Sanjeev Kumar (20141063)Saurabh Ojha (20141064)Vikash Kumar (20141072)

Group 10

Page 2: Machine learning
Page 3: Machine learning

What is Machine Learning?

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Speech to Text as a part of Machine Learning

Page 4: Machine learning

Components of Speech To Text Interface

Page 5: Machine learning

Voice recognition Algorithm

i)Hidden Markovii) N-gram

•It is currently the most successful and most flexible approach to speech recognition.•Speech to text application is adapted to input messages in English.

Page 6: Machine learning

• What is Hidden Markov ?• Urn & Ball Model • Development of Hidden

Markov Library

Hidden Markov

Page 7: Machine learning

N-Gram Language Model

•N-gram : contiguous sequence of n items from given sequence of text or speech.•What is language model?•Markov Assumption: P(w1 w2 ……wn) ≈ ∏iP(wi/wi-k….wi-1)•Simplest case of markov model is unigram.

Page 8: Machine learning

• Unigram Model: P(w1 w2…….wn)≈ ∏P(wi)• Bigram Model: P(wi/w1 w2…….wn) ≈ P(wi/wi-1)• We can extend this to trigram, 4-gram, 5-grams and continue.

Page 9: Machine learning

• Hands free computing• Education and daily life• Blindness & Education

Problems faced in today’s world

Page 10: Machine learning

Why ANDROID Platform?

Page 11: Machine learning

Architecture

Page 12: Machine learning

Speech Recognition

Page 13: Machine learning

MAIN PARTS OF THE PROJECT

A. Voice Recognition Activity class

Page 14: Machine learning

B. SMS class

Page 15: Machine learning

C. XML files

Page 16: Machine learning

Some Part of Code to trigger Speech to Text

1. Speech is turned into a list

2. Voice recognition result.

Page 17: Machine learning

• Operating System used for development id free of cost and so is the eclipse ide used as an interface for application development.

• Free use and adaptation of operating system to manufacturers of mobile devices. • Equality of basic core applications and additional applications in access to

resources. • Optimized use of memory and automatic control of applications which are being

executed. • Quick and easy development of applications using development tools and rich

database of software libraries. • High quality of audiovisual content, it is possible to use vector graphics, and

most audio and video formats. • Ability to test applications on most computing platforms, including Windows,

Linux. Thus saving time and money.

Economic Feasibility

Page 18: Machine learning

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Page 19: Machine learning

Thank you


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