Titlebook: Speech Processing and Soft Computing; Sid-Ahmed Selouani Book 2011 Springer Science+Business Media, LLC 2011 Artificial neural networks.Ge

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Introduction,re confronted with imprecise and/or distorted information. In contrast hard computing, i.e., conventional computing, requires a precisely stated analytical model and is often valid under specific assumptions and for ideal cases.

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Speech Enhancement Paradigmnt varies according to the needs of specific applications, such as to increase the overall speech quality or intelligibility, to reduce listener fatigue or to improve the global performance of an ASR embedded in a voice communication system. This chapter begins by giving a background on noise and it

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Variance of the Reconstruction Error Techniquensidered by the model, relevant components of speech may be lost. Conversely, if more PCs are selected, the model will be ineffective and the noise will remain. The purpose of this chapter is to present a signal subspace decomposition method using a Variance of Reconstruction Error (VRE) criterion t

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Artificial Neural Networks and Speech Recognitionmade neural networks very popular in the field of speech processing. In this chapter, the usefulness of a neural network using autoregressive backpropagation and time-delay components (AR-TDNN) is illustrated. Combined with HMMs, the AR-TDNN is incorporated in a flexible hybrid structure which attem