Speaker identification system with voice controlled functionality
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Speaker identification system with voice controlled functionality

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Speaker identification system with voice controlled functionality Speaker identification system with voice controlled functionality Presentation Transcript

  • SPEAKER IDENTIFICATION SYSTEM WITH VOICE – CONTROLLED FUNCTIONALITY
  • IntroductionObjective  To develop a speaker identificationsystem and control the system using a person’svoice.Platform  MatlabImplementation of Artificial Neural Networks(ANN) for pattern classificationFeature extraction  MFCC 2
  • Experimental Setup Sound Recorder Feature ExtractionSpeech Wav File MFCC Artificial Neural Network Subsystem Test Train 3
  • Signal ProcessingBuilt - in MATLAB function‘wavrecord.m’ The recorded samples serve as input tothe next stage, which is the Mel –Frequency Cepstral Analysis. 4
  • Feature ExtractionMel – Frequency Cepstral Coefficients (MFCC) MFCCs are based on the known variation of thehuman ear’s critical bandwidths with frequencyLinear at low frequencies and logarithmic at highfrequencies 5
  • MFCC Block DiagramSpeech Frame Frame Windowing FFT Blocking Mel Cepstrum Mel Mel–Freq. Spectrum Cepstrum Wrapping Spectrum 6
  • Steps of MFCC1. Frame Blocking2. Windowing3. Fast Fourier Transform (FFT )4. Mel–Frequency Wrapping5. Cepstrum Auditory Toolbox - mfcc.mceps=mfcc(input, sampling rate, [frame rate]) 7
  • Artificial Neural Networks (ANN)General models of how human brain processesinformation.Layered architecture  Consists of nodescorresponding to neurons and of weightscorresponding to connections between neurons“Learning” rule  Weights are adjusted on thebasis of a series of training patterns 8
  • Probabilistic Neural Network (PNN) Feed – forward neural network Provides a general technique to solve pattern classification problems Develops distribution function to estimate the likelihood of an input pattern being within several given categories. Created in MATLAB using ‘newpnn’ net = newpnn(p,t) 9
  • Schematic Diagram 10
  • ConclusionImplementation difficult due to variabilityin speech signalPossible improvement using noisecancellation techniques  Weiner Filter,Adaptive Filters 11
  • ReferencesL.Rabiner, B. H. Juang – Fundamentals of SpeechRecognitionC. P. Lim, S.C. Woo – Speech Recognition usingNeural Networks. IEEE Trans. on Acoustics,Speech and Signal Processing - 2000.Khalid Saeed and Mohammed Kheir Nammous –A Speech and Speaker Identification System.IEEE Trans. on Industrial Electronics - 2007. 12
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