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Neural Network

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ARTIFICIAL NEURAL NETWORK FOR SPEECH RECOGNITION
One of the problem found in speech recognition is recording samples never produce identical waveforms. This happens due to different in length, amplitude, background noise, and sample rate. This problem can be encountered by extracting speech related information using Spectogram. It can show change in amplitude spectra over time. For example in diagram below: X Axis : TimeY Axis : FrequencyZ Axis : Colour intensity represents magnitude | | A cepstral analysis is a popular method for feature extraction in speech recognition applications and can be accomplished using Mel Frequency Cepstrum Coefficient (MFCC) analysis Input Layer is 26 Cepstral CoefficientsHidden Layer is 100 fully-connected hidden-layerWeight is range between -1 and +1 * It is initially random and remain constantOutput : * 1 output unit for each target * Limited to values between 0 and +1 | |

First of all, spoken digits were recorded. Seven samples of each digit consist of “one” through “eight” and a total of 56 different recordings with varying length and environmental conditions. The background noise was removed from each sample. Then, calculate MFCC using Malcolm Slaney’s Auditory Toolbox which is c=mfcc(s,fs,fix((3*fs)/(length(s)-256))).
Choose intended target and create a target vector. If the training network recognise spoken one, target has a value of +1 for each of the known “one” stimuli and 0 for everything else. This will be supervised learning where intended target is chosen and a target vector is created. In a finite amount of time, the perceptron will successfully learn to distinguish between stimuli of an intended target or not.
Calculate Hidden Layer: h = sigmoid (W * s + bias)
Calculate Response: o = sigmoid (v * H + bias) sigmoid(x)=1/(1+e-x) and its maps values between ) and +1.
To Calculate error which is the difference between target and response use * t-o * t will be either 0 or 1 * o will be between 0 and +1
Update Weight * v = vprevious + γ(t-o)hT * v is weight vector between hidden layer units and output * γ (gamma) is learning rate

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