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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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