Voice Recog

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VOICE RECOGNITION USING

ARTIFICIAL NEURAL NETWORKS

BYMs. SONA PREM BHASIN

E-305

UNDER THE GUIDANCE OFMRS. KIRAN DANGE


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INTRODUCTION

Voice recognition uses the acoustic features of speech that have been found to differ betweenindividuals. It makes possible to use the speaker's voice to verify

their identity and control access to services.Voice recognition systems employ two styles of spoken input:

Text-dependent : require the speaker to say keywords or sentences.

Text-independent : do not rely on a specific textbeing spoken.


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VOICE RECOGNITION Voice recognition encompasses Verification & Identification.

Verifies the identity claimed from persons voice.

There is no identity claim

The system decides who the person is,

What Group the person is a member of,Or that the person is unknown.


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SPEECH PROCESSING


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VOICE RECOGNITION STEPS INVOLVED


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SOURCES OF VERIFICATION ERROR IN VOICE RECOGNITION

Misspoken or misread prompted phrases

Extreme emotional states

Time varying microphone placement

Poor room acoustics

Channel mismatch

Sickness

Ageing


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WHY USE ARTIFICIAL NEURALNETWORKS ?

Massive parallelismLearning ability

RobustnessInherent contextual information processing AdaptabilityGeneralization abilityFault tolerance capabilityLow energy consumption


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ARCHITECTURE OF NEURALNETWORKS

Network Layers- Single layer - Multilayer


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Feed Forward NetworksSignals travel in one way only


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Feedback Networks

Signals travel in both directions


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PerceptronsCalculates weighted sum of inputs and compares it to athreshold


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BACK PROPAGATION NETWORK


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BACK PROPAGATION ALGORITHMInitialise the weights to small random

values.Randomly choose an input pattern.Propagate the signal forward through the

network.Compute error in the output layer.Compute the errors for the preceding layersby propagating the errors backwards.Update weights.Go to step 2 and repeat for the next patternuntil the error in the output layer is below a

pre-specified threshold or a maximum


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Implementation of applicationsinvolve :

ApplicationProblem Formulation

Algorithm AnalysisNeural Model

Architecture

Implementation


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APPLICATIONS OF NEURAL NETWORKS

Neural Networks in practice:Sales forecastingIndustrial process controlCustomer research

Target marketingNeural Networks in Medicine:

Modeling and diagnosing cardiovascular systemInstant physician


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APPLICATIONS OF VOICE RECOGNITION

CommunicationCorporate customer relationsBanking transactionsEducationMilitaryPolicingSecurity Systems


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FUTURE SCOPE FOR ANNs

User-specific systems

Genetic engineering

Man machine interface


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CONCLUSIONIf the 21 st century is to be the age of intelligent machines,

artificial neural networks will become an integral part of ourlives.

In order that software engineers can lead us to this promised life they must begin by utilising the emerging technology of

artificial neural networks. As users begin to take advantage of the technology anddemand grows for better software, manual interfaces of allkinds will become a thing of the past. This method of using

Voice Recognition for interaction is well on its way tobecoming a reality.

But even this is a temporary stage in the evolution of man-machine interaction. One day there will be a symbiosisbetween the two.


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REFERENCES[1] J.P. Campbell, JR, Speaker Recognition: A Tutorial,Proceedings of the IEEE, Vol. 85, No. 9, September 1997.

[2] Patricia Melin, Jerica Urias , Daniel Solano, Miguel Soto, VoiceRecognition with Neural Networks, Type-2 Fuzzy Logic andGenetic Algorithms

[3] R.L. Kashyap , Speaker Recognition from a Unknown Utteranceand Speaker- Speech Interaction, IEEE Tran on Acoustics, Speechand Signal Processing, vol. assp-24, no. 6, pp. 481-488, December 1976.

[4] H.Gish and M.Schmidt,Text -independent speaker identification, IEEE Signal Process. Mag., vol.18, pp.18 -32, Oct,2002

[5] Morgan, D., Scofield,C., and Adcock,J . (1991). Multiple NeuralNetwork Topologies Applied to Keyword Spotting, In Proc. IEEEInternational Conference on Acoustics, Speech, and SignalProcessin 1991.


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Voice Recog