Hybrid model decomposition of speech and noise in a radial basis function neural model framework
نویسندگان
چکیده
This paper focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the precence of additive car noise. The technique applied is based on a combination of the Hidden Markov Model (HMM) decomposition method [ 11, for speech recognition in noise, developed by Varga and Moore from DRA and the hybrid (HMM/RBF) recognizer [2], containing Hidden Markov Models and Radial Basis Function (RBF) Neural Networks, developed by Singer and Lippmann from MIT Lincoln Lab. We modified the hybrid recognizer to fit into the decomposition method to achieve high performance speech recognition in noisy environments. Our approach has been denoted the Hybrid Model Decomposition method and it provides an optimal method for decomposition of speech and noise by using a set of speech pattern models and a noise model(s), each realized as an HMM/RBF pattern model.
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