applications of artificial neural networks in voice recognition and nettalk

INTRODUCTIONFROM HUMAN NEURONS TO ARTIFICIAL NEURONS

APPLICATION FIELDSCONCLUSION

APPLICATIONS OF ARTIFICIAL NEURALNETWORKSAPPLICATIONS

ARTIFICIAL NEURAL NETWORKS



Artificial Neural NetworkWhy Use Neural Networks?Advantages

What is a Neural Network?

A neural network is a processing device, either analgorithm, or actual hardware, whose design wasmotivated by the design and functioning of human brainsand components thereof.The abilities of different networks can be related to theirstructure, dynamics and learning methods.There are many different types of Neural Networks, eachof which has different strengths particular to theirapplications.





Artificial Neural Network

Historical background.First Attempts.Promising and Emerging TechnologyToday





Features Of ANN

Their ability to represent nonlinear relations makes themwell suited for non linear modeling in control systems.Adaptation and learning in uncertain system through offline and on line weight adaptation Parallel processingarchitecture allows fast processing for large-scale dynamicsystem.Neural network can handle large number of inputs and canhave many outputs.

SHAJEER.K.B ARTIFICIAL NEURAL NETWORKS




Advantages

Adaptive learningSelf-OrganisationReal Time OperationFault Tolerance via Redundant Information Coding




From Human Neurons to Artificial NeuronsNeural Network ModelsNeural Network Models

From Human Neurons to Artificial Neurons





From Human Neurons to Artificial Neurons

InputsWeightsThresholdActivation Function





Multilayer perceptron





Two hidden layer multilayer perceptron




Applications in SpeechNeural Networks in MedicineImage CompressionProcess Modelling and ControlTargeted Marketing and Financial Forecasting

Applications

Voice Recognition - Transcribing spoken words into ASCIItext.Image Compression - Because neural networks canaccept a vast array of input at once, and process it quickly,they are useful in image compression.Medical Diagnosis - Assisting doctors with their diagnosisby analyzing the reported symptoms and/or image datasuch as MRIs or X-rays.





Process Modeling and Control - Creating a neural networkmodel for a physical plant then using that model todetermine the best control settings for the plantTargeted Marketing - Finding the set of demographics,which have the highest response rate for a particularmarketing campaign.Financial forecasting - Using the historical data of asecurity to predict the future movement of that security.





Voice Recognition

Speech recognition: The objective is to determine thesequence of sound units from the speech signal so that thelinguistic message in the form of text can be decoded fromthe speech signal.Speech synthesis: The objective is to determine thesequence of sound units corresponding to a text so that agiven text message can be encoded to a speech signal.Speaker identification: the objective is to determine theidentity of the speaker from the speech signal.





NETTALK

used to generate synthetic speechdeveloped by Terry Sejnowski and Charles RosenbergThe data ârepresentation scheme employed allows atemporal pattern sequence to be represented spatiallyNETtalk Data Representation





NETTALK

ConsiderExample:FIND FIEND FRIEND FEINT

a sliding window technique for representing words aspatterns





Neural networks:medicine





Neural networks in medicine

Artificial Neural Networks (ANN) are currently a ’hot’research area in medicine and it is believed that they willreceive extensive application to biomedical systems in thenext few years.At the moment, the research is mostly on modelling partsof the human body and recognising diseases from variousscans (e.g. cardiograms, CAT scans etc.)Neural networks are ideal in recognising diseases usingscans since there is no need to provide a specificalgorithm on how to identify the disease.





CARDIOVASCULAR SYSTEM:MODELLING

Neural Networks are used experimentally to model thehuman cardiovascular system.Diagnosis can be achieved by building a model of thecardiovascular system of an individual and comparing itwith the real time physiological measurements taken fromthe patient.A model of an individual’s cardiovascular system mustmimic the relationship among physiological variables (i.e.,heart rate,blood pressures, and breathing rate) at differentphysical activity levels.If a model is adapted to an individual, then it becomes amodel of the physical condition of that individual.





ELECTRONIC NOSES:DIAGNOSING

Electronic noses have several potential applications intelemedicine.The electronic nose would identify odours in the remotesurgical environment.Telemedicine is the practice of medicine over longdistances via a communication link.





Image Compression





BOTTLENECK ARCHITECTURE





QUANTIZATION





HOW DOES A NETWORK LEARN TO DO THIS?

The goal of these data compression networks is tore-create the input itself.This data is presented over and over, and the weightsadjusted, until the network reproduces the image relativelyfaithfully.Once training is complete, image re-construction isdemonstrated in the recall phase.we can continue to train the network if the output is not ofhigh enough quality.





ORIGINAL IMAGERECONSTRUCTED

IMAGE





PROCESS MODELLING AND CONTROLAPPLICATIONDS

ANN is able to emulate the information processingcapabilities of biological neural system.Artificial neural networks are implemented as softwarepackages in computers and being used to incorporate ofartificial intelligence in control system.ANN has overcome many of the difficulties that tconventional adaptive control systems suffer while dealingwith non linear behavior of process.





ANN DESIGN PARAMETERS INCLUDE

The interconnection strategy/network topology/networkstructure.Unit characteristics (may vary within the network andwithin subdivisions within the network such as layers).Training procedures.Training and test sets.Input/output representation and pre- and post-processing.





LEARNING TECHNIQUES

Forward modelingThe basic configuration used for non-linear systemmodeling and identification using neural network.The number of input nodes specifies the dimensions of thenetwork input.In system identification context, the assignment of networkinput and output to network input vector.





DIRECT INVERSE MODELING

This approach employs a generalized model suggested byPsalters et al.to learn the inverse dynamic model of theplant as a feed forward controller.Here, during the training stage, the control input arechosen randomly within there working range.The corresponding plant output values are stored, as atraining of the controller cannot guarantee the inclusion ofall possible situations that may occur in future.





Targeted Marketing and Financial Forecasting





TARGETED MARKETING AND FINANCIALFORECASTING

A simple back-propagation network of three layers, and it istrained and tested on a high volume of historical marketdata.The challenge here is not in the network architecture itself,but instead in the choice of variables and the informationused for training. Neural Networks in businessUsing neural networks for business purposes, includingresource allocation and scheduling.There is also a strong potential for using neural networksfor database mining, that is, searching for patterns implicitwithin the explicitly stored information in databases.





MARKETING

The Airline Marketing Tactician is a computer system madeof various intelligent technologies including expertsystems.A feed forward neural network is integrated with the AMTand was trained using back-propagation to assist themarketing control of airline seat allocations.The adaptive neural approach was amenable to ruleexpression.The application’s environment changed rapidly andconstantly, which required a continuously adaptive solution.




ReferenceGratitude

CONCLUSION

The computing world has a lot to gain from neuralnetworks.The neural network can be used to design a controller,robotics, industrial manufacturing, aerospace and severalothers.The ability of a feedback network to store patterns can beimproved, if we can exploit the chaotic nature of thenetwork dynamics.Due to inherent non-linearity and also due to the learningability, neural networks appear to be promising in somedecision making applications.




ReferenceGratitude

REFERENCE

Artificial Neural Networks and its Applications Girish KumarJha I.A.R.I Newdelhi 110 012 [email protected] Networks:A Comprehensive Study 2nd EditionSimon HaykinArtificial Neural Networks:B Yegananarayana




ReferenceGratitude

Thank You


applications of artificial neural networks in voice recognition and nettalk

Documents

speechneural networks

different strengths

fast processing

processing device

learning methods

controltargeted marketing

spoken words

largescale dynamicsystem