Speaker Independent Speech Recognition Using Neural Network
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Speaker Independent Speech Recognition Using Neural Network
In spite of advances accomplished throughout the last few decades, automatic speech recognition (ASR) is stilla challenging and difficult task when the systems are applied in the real world. Different requirements for various applications drive the researchers to explore for more effective ways in the particular application. Attempts to apply artificial neural networks (ANN) as a classification tool are proposed to increase the reliability of the system. This project studies the approach of using neural network for speaker independent isolated word recognition on small vocabularies and proposes a method to have simple MLP as speech recognizer. Our approach is able to overcome the current limitations of MLP in the selection of input buffers' size by proposing a method on frames selection. Linear predictive coding (LPC) has been applied to represent speech signal in frames in early stages. Features from the selected frames are used to train the multilayer perceptrons (MLP) feed-forward back-propagation (FFBP) neural network during the training stage. same routine has been applied to be speech signal during the recognition stage and the unknown test pattern will be classified to one of the nearest pattern. In short, the selected frames represent the local features of the speech signal and all of them contribute to the global similarity for the whole speech signal. The analysis, design and the PC based voice dialling system is developed using MATLAB®.
Readings in Speech Recognition
Speech recognition by machine : a review / D.R. Reddy -- The value of speech recognition systems / W.A. Lea -- Digital representations of speech signals / R.W. Schafer and L.R. Rabiner -- Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences / S.B. Davis and P. Mermelstein -- Vector quantization / R.M. Gray -- A joint synchrony-mean-rate model of auditory speech processing / S. Seneff -- Isolated and connected word recognition : theory and selected applications / L.R. Rabiner and S.E. Levinson -- Minimum prediction residual principle applied to speech recognition / F. Itakura -- Dynamic programming algorithm optimization for spoken word recognition / S. Hakoe and S. Chiba -- Speaker-independent recognition of isolated words using clustering techniques / L.R. Rabiner [and others]Two-level DP-matching : a dynamic programming-based pattern matching algorithm for connected word recognition / H. Sakoe -- The use of a one-stage dynamic pr ...
Advances In Pattern Recognition Systems Using Neural Network Technologies
Contents:A Connectionist Approach to Speech Recognition (Y Bengio)Signature Verification Using a “Siamese” Time Delay Neural Network (J Bromley et al.)Boosting Performance in Neural Networks (H Drucker et al.)An Integrated Architecture for Recognition of Totally Unconstrained Handwritten Numerals (A Gupta et al.)Time-Warping Network: A Neural Approach to Hidden Markov Model Based Speech Recognition (E Levin et al.)Computing Optical Flow with a Recurrent Neural Network (H Li & J Wang)Integrated Segmentation and Recognition through Exhaustive Scans or Learned Saccadic Jumps (G L Martin et al.)Experimental Comparison of the Effect of Order in Recurrent Neural Networks (C B Miller & C L Giles)Adaptive Classification by Neural Net Based Prototype Populations (K Peleg & U Ben-Hanan)A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes: A Pilot Study (L Wiskott & C von der Malsburg)and other papers Readership: Computer scientists and engineers.