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Text Independent Speaker Recognition with Added Noise

Text Independent Speaker Recognition with Added Noise. Jason Cardillo & Raihan Ali Bashir April 11, 2005. Problem Definition. Many methods for Text Independent Speech Recognition (MFCC, Gaussian, Markov etc) Few methods perform well with noisy speech samples. Project Goal.

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Text Independent Speaker Recognition with Added Noise

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  1. Text Independent Speaker Recognition with Added Noise Jason Cardillo & Raihan Ali Bashir April 11, 2005

  2. Problem Definition • Many methods for Text Independent Speech Recognition (MFCC, Gaussian, Markov etc) • Few methods perform well with noisy speech samples.

  3. Project Goal • Implement Text Independent Speaker Recognition system robust to noise effect. • The suggested implementation method is Recurrent Neural Nets (RNN)

  4. Definition of RNN • Recurrent networks (RNs) are models with bi-directional data flow. While a feed-forward network propagates data linearly from input to output, RNs also propagate data from later processing stages to earlier stages. • In a fully recurrent network, every neuron receives inputs from every other neuron in the network. These networks are not arranged in layers. Usually only a subset of the neurons receive external inputs in addition to the inputs from all the other neurons, and another disjunct subset of neurons report their output externally as well as sending it to all the neurons. These distinctive inputs and outputs perform the function of the input and output layers of a feed-forword or simple recurrent network, and also join all the other neurons in the recurrent processing.

  5. Why RNN for our Purpose? • RNN captures long-term contextual effect over time • Therefore can use temporal context to compensate for missing data. • Also allows a single net to perform both imputation and classification.

  6. Corrupted Data Solution X= missing data at time t; y = learning rate; Vjm = indicates recurrent links from a hidden unit to the missing input; hid = activation of hidden unit j at time t-1 Input missing values for the next frame through the recurrent links after a feed-forward pass.

  7. Corrupted Data Solution(cont’d) • English Translation of previous slide: • Basically fill in missing data with average of all of the non-corrupted frames. • Accomplished by factoring sum squared error between correct targets and RNN output of each frame • Back propagate this result through time to fix corrupted inputs

  8. System Architecture

  9. Performance Testing • Measured by comparing original error of signal to error remaining after passing through the system.

  10. References [1] Parveen,S, Green, P.D.Speech Recognition with Missing Data using Recurrent Neural Nets. University of Sheffield Dept of Computer Science. http://www.dcs.shef.ac.uk/~shahla/nips002.pdf [2] http://encyclopedia.laborlawtalk.com/Neural_network [3] Recurrent Neural Networks http://www.idsia.ch/~juergen/rnn.html [4] Jain,BJ, Wysotzki F. Learning with Neural Networks in the Domain of Graphs. Technical University of Berlin. http://ki.cs.tu-berlin.de/~bjj/fgml04.pdf

  11. Questions

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