1. INTELLIGENT MACHINE LEARNING APPROACH FOR NLP
Om Sakthi
Adhiparasakthi Engineering College, Melmaruvathur –
Department of Computer Science and Engineering
INTELLIGENT MACHINE LEARNING APPROACH FOR NLP
GUIDE TEAM
Mr.R.Balamurugan.M.E., Suresh.C
Assistant Professor, Tamilarasan.C
Dept of CSE, APEC Thiruvikraman.G

2. ABSTRACT
Speech recognition is a challenging problem in Artificial Intelligence. It is a solved problem in some restricted settings, for example spoken words are limited to a small vocabulary. In this case speech recognition systems are already in common use, e.g. for command and name recognition. Error-free recognition of unrestricted continuous speech remains a difficult & unsolved problem. To provide flexibility in recognition, the system can need a self learning program internally to understand the voice modulation by listening and updating different pronunciations.

3. INTRODUCTION
Today speech recognition technology finds broad applications in telephone & software-based systems. Different research and industrial sectors believe that the rapid advancements in the technology have contributed to the ever robust, evolving and advanced speech recognition products. Favorite search engines can search the text of millions of Web pages to produce results, it would be far better if they could do a similar search over the millions of recorded interviews and conversations that frequently appear on news channels and talk shows.
Although speech recognition technology has met with much success, companies like Kyocera have repeatedly highlighted its failure in this area and it becomes a very challenging task to provide service for customers through speech recognition system.

4. BASIC CONCEPTS
1.Artificial Intelligence : study and design of intelligent agents & describe a property of machines or programs.
2.Speech Recognition : process of converting a speech signal to a sequence of words, by means of an algorithm implemented as a computer program.
3.Natural Language Processing : processing of natural language is an area of artificial intelligence.
4.Machine Learning : a branch of artificial intelligence, is about the construction and study of systems that can learn from data.
5. Neural Networks : to simulate the behavior of the brain using interconnected abstractions of the real neurons.

5. EXISTING SYSTEM PROBLEMS
Recognition limited to a small vocabulary (restrictions in recognition to avoid confusion)
Static in functions.
It may confuse and process multiple commands when it is placed in noisy area.

6. OBJECTIVES OF PROPOSED SYSTEM
To provide a customized speech recognition system.
To provide system with manual learning to avoid vocabulary limitations and constraints.
It recognize by differentiating background noise based on frequency depth.

7. PROPOSED SYSTEM PROBLEM DEFINITION
The problem defines that how to provide flexibility in recognition in order to get reliable information with learning mechanism based on perception.
PROBLEMS TO SOLVE
1.Homonyms : words that sound the same but have different meanings “hear” and “here”, for example.
2.Context : On what topic is the speaker focusing? 3.Accents : Due to regional differences in spoken languages at a global level. The same person may pronounce the same words differently in emotional states. 4.Environmental interference: It is difficult to communicate effectively in a noisy room or a busy office.

8. ARCHITECTURE

9. REQUIREMENTS Hardware Requirements
Computer connected with Speaker and Microphone
160GB Hard Disk
2GB RAM
2GHz Clock Speed
Intel® Core (TM) 2 Duo
14 Inch color monitor
Software Requirements
Windows XP /7
C#.NET & MS-Speech SDK 5.1
SQL 2005 Database

10. DESIGN ENGINEERING
In this topic the various UML diagrams that are used for the implementation of the project are discussed.
The various UML diagram used here are
Use Case diagram
Sequence diagram
Activity diagram
Data Flow diagram

11. USECASE DIAGRAM

12. SEQUENCE DIAGRAM

13. ACTIVITY DIAGRAM

14. DATAFLOW DIAGRAM

15. MODULES The system contains the following modules
Login and Register Users
Task and Data Set
Data Representation
Initializing Recognition & Processing

16. LOGIN & REGISTRATION In computer security,
A login or logon refers to the credentials required to obtain access to a computer system or other restricted area. Logging in or on and signing in or on is the process by which individual access to a computer system is controlled by identifying and authenticating the user through the credentials presented by the user in order to access that system (e.g., a computer or a website).
It is an integral part of computer security procedures.
In mathematical logic and theoretical computer science a register machine is a generic class of abstract machines used in a manner similar to a Turing machine.

17. LOGIN & REGISTRATION
SCREENSHOTS
LOGIN REGISTRATION

18. TASK & DATA SET
The two tasks provide the basis for design of an information technology framework that is capable to identify and disseminate information.
SENTENCE IDENTIFICATION - The first task identifies and extracts informative topics.
RELATION IDENTIFICATION - The second performs faster a fine grained classification of these sentences according to the semantic relations that exists between words and occurrence.

19. SENTENCE IDENTIFICATION RELATION IDENTIFICATION
The sentence identification is a task which is similar to a scan of sentence contained in the abstract of an article in order to present to the user-only sentence that are identified as containing relevant information.
It has deeper semantic dimension and it is focused on complex relations in the sentence already selected as being informative. The approach used to solve the two proposed task is based on NLP and ML techniques. In a standard supervised ML setting, a training set and a test set are required. The training set is used to train the ML algorithm and the test set to test its performance.

20. DATA REPRESENTATION BAG-OF-WORDS REPRESENTATION
The bag-of-words (BOW) representation is commonly used for text classification tasks. It is a representation in which features are chosen among the words that are present in training data.
Selection techniques are used in order to identify the most suitable words as features.
NLP CONCEPT REPRESENTATION
Lemma (logic), which is simultaneously a premise for a contention above it and a contention for premises below it. We choose to use lemmas because there are a lot of inflected form for the same word and the lemmatized form will gives us the same base from all for all of them. Another reason is to reduce the data sparseness problem.

21. DATA REPRESENTATION
Set your command

22. INITIALIZING SPEECH REGOGNITION & PROCESSING
The user can initialize recognition and perform processing which is based on the predefined data or query in database manually assigned by the user.
This module works on the basis of Neural Networks as preprocessing features such as transformation and dimensionality reduction.
Neural networks emerged as an attractive acoustic modeling approach in SR. It has been used in many aspects of speech recognition such as phoneme classification, isolated word recognition, and speaker adaptation.
In contrast to HMMs, neural networks make no assumptions about feature statistical properties and have several qualities making them attractive recognition models for speech recognition in a natural and efficient manner.

23. PROCESS INVOLVED IN RECOGNITION
SPEECH ANALYSIS
1. Morphological analysis
2. Syntactic analysis
3. Semantic analysis

24. PROCESS INVOLVED IN RECOGNITION
The morphological analysis splits a sentence into words. Each word is looked up in a dictionary in order to determine its word class and inflection. This information is used as input for the syntactic analysis.
The syntactic analysis checks that the words form a legal sentence. The rules of which sentences are legal in the language are expressed in the form of a grammar. In this project are used so-called extended state diagrams for the purpose. They are also called ATN- grammars (an abbreviation of Augmented Transition Networks). The result of the syntactic analysis is input for the semantic analysis.
The semantic analysis determines the meaning of a sentence, among other things by looking up the meaning of the individual words of the sentence.

25. PROCESS INVOLVED IN RECOGNITION
Process happens in normal speech chain Speech Chain Process in system

26. PROCESS INVOLVED IN RECOGNITION
Example : The dog likes a man

27. PROCESS INVOLVED IN RECOGNITION
INPUT PERCEPTION & TRANSLATION
Perception (from the Latin perceptio, percipio) is the organization, identification and interpretation of sensory information in order to represent and understand the environment. All perception involves signals in the nervous system, which in turn result from physical stimulation of the sense organs
Machine translation (translation from one natural language to another)

28. INITIALISING SPEECH RECOGNITION & PROCESSING
Initializing Engine
Result after processing

29. ALGORITHM HMM - EXISTING SYSTEM
The observation is a probabilistic function of the state.
Situation : User State
State : bad, neutral, or a good
Viterbi algorithm - Searching for the most probable path
Forward algorithm - Probability of a sequence
Continuous observation probability density function is used.
LIMITATIONS
Data intensive
Computationally intensive

30. ALGORITHM . . . DEANNT+ - PROPOSED SYSTEM
It is derived from DE Algorithm
DE Algorithm without adaptive selection control parameters in ANN Training results in problem.
To overcome the problem, the modification of DE is provided with control parameters & multiple trial vectors, is renamed as DEANNT+.
In ANN Training, it is helpful in classifying parity-p problems.
ADVANTAGES
Efficient memory utilization
Lower computational complexity
Lower computational effort
Effective in nonlinear constraint optimization
Optimizing multimodal problems
The multiple trial vectors technique increases the probability of generating a better solution because a greater number of temporary solutions are generated around the existing solutions.

31. Analysis efficiency of algorithm based on parameters
ALGORITHM EFFICIENCY
Analysis efficiency of algorithm based on parameters
where,
N - number of samples in the analysis frame
M - number of samples shift between frames
P - LPC analysis order
Q - dimension of LPC derived cepstral vector
K - number of frames over which cepstral time
derivatives are computed

32. Comparison of Existing Vs. Proposed System
RESULT EVALUATION
Comparison of Existing Vs. Proposed System

33. APPLICATIONS
Military sector : High performance fighter aircraft, Helicopters, Battle management, Training air traffic controllers, Telephony and other domains, people with disabilities.
Education Sector : Enabling students who are physically handicapped and unable to use a keyboard to enter text verbally.
Outside education sector : Computer and video games, Gambling, Precision surgery.
Domestic sector : Oven, refrigerators, dishwashers and washing machines.
Dictation : Dictation systems on the market accepts continuous speech input which replaces menu system.

34. FUTURE
ENHANCEMENTS
Speech recognition technology improvements in future,
The first is improvement of computer interaction interfaces. Today, hardly any computer comes pre-loaded with a speech recognition system yet the day is not far off when speech recognition systems will be common in most computer systems.
The second is as a common proxy to current GUI systems in the future this itself would revolutionize the way we interact with computers in our everyday work.
The third potential application is in speech recognition systems as aids for visually-challenged and writing-impaired people, to help them express themselves and obtain an education.

35. CONCLUSION
We have also encountered a number of practical limitations which hinder a widespread deployment of application and services.
There is now increasing interest in finding ways to bridge such a performance gap.
Although these areas of investigations are important, the significant advances will come from studies in acoustic-phonetics, speech perception, linguistics, and psychoacoustics.
Future systems need to have an efficient way of representing, storing, and retrieving knowledge required for natural conversation.

36. REFERENCES
[1] Adam Slowik,” Application of an Adaptive Differential Evolution Algorithm With Multiple Trial Vectors to Artificial Neural Network Training,” IEEE Transactions on Industrial Electronics, vol. 58, No. 8, August 2011
[2] M. M. EI Choubassi, H. E. EI Khoury, C. E. Jabra Alagha, J. A. Skaf and M. A. Al-Alaoui,” Arabic Speech Recognition Using Recurrent Neural Networks”.
[3] Itamar Arel, Derek C. Rose, and Thomas P.Karnowski,” Deep Machine Learning,” A New Frontier in Artificial Intelligence Research.
[4] Simon Corston-Oliver, Michael Gamon and Chris Brockett,” A machine learning approach to the automatic evaluation of machine translation,” Microsoft Research.
[5] Tan Lee and P.C. Ching,” A Neural Network Based speech Recognition For Isolated Cantonese Syllables”.