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SIGNAL MODELING IN VOICE RECOGNITION

SIGNAL MODELING IN VOICE RECOGNITION. Done By: Ayman Rabee. Signal Modeling represents the process of converting sequence of speech samples to observation vectors represents events in probability space. This Model can be divided into 4 stages :. Spectral Shaping:.

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SIGNAL MODELING IN VOICE RECOGNITION

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  1. SIGNAL MODELING IN VOICE RECOGNITION Done By: Ayman Rabee

  2. Signal Modeling represents the process of converting sequence of speech samples to observation vectors represents events in probability space.This Model can be divided into 4 stages:

  3. Spectral Shaping: • Two main operations involved here: • A/D conversion. • Digital filtering.

  4. The characteristics of the Digital filter is: But normally one coefficient of the digital filter is used, so it will be:

  5. Spectral Analysis • Digital Filter Bank. • Linear Prediction Coefficient. Here we will deal with the most famous algorithms which are:

  6. Digital Filter Bank • The D.F.B is implemented as a linear phase band filters, and represented as: The main motivation behind D.F.B is that What is calledPLACE THEORY: the position of maximum displacement of pure tones is proportional to the to the logarithm of the frequency of the tone

  7. Two ways of mapping acoustic freq. f , to perceptual freq. scale: • Bark Scale and defined as: • Mel Scale and defined as:

  8. Linear Prediction Coefficient Linear prediction LP is used to represent the spectral characteristics of speech. The prediction error is defined as Given a signal, s(n), we seek to model the signal as a linear combination of its previous samples:

  9. The last equation can be solved to give the LP coefficient, the solution is done by a well-known method “Covariance Method”.

  10. Parameter Transformation The basic idea behind this model is that the parameters that have been measured using some methods need some sort of smoothing and concatenation to form signal parameter. • Differentiation. • Concatenation.

  11. Differentiation The trend is to increase he performance of the signal model. This can be done by including higher order time derivatives of the signal.

  12. Concatenation Concatenation is defined the process of creation a single parameter vector that contains all desired signal parameters. Measurement Matrix

  13. Delay Matrix Weighting Matrix The process of filtering the measurement vector is defined as a pseudo-convolution operator:

  14. STATISTICAL MODELING Now, we generate the signal parameter using the previous techniques. To evaluate the probability of the Observation sequence a well-Known technique is used “Hidden Markov Model” HMM, which is characterized by: A: Transition Probability matrix (N x N) B: Observation symbol Probability Distribution matrix (N x M) PI: Initial State Distribution matrix (N x 1)

  15. The problem, which HMM is addressed to solve is: P(O/L) Where O = {o1, o2, o3, o4 ...} Speech Model L = (A, B, PI ). The probability is found using a forward/backward technique.

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