Speech Processing and Soft Computing by Sid-Ahmed Selouani

By Sid-Ahmed Selouani

Speech Processing and gentle Computing comprises insurance of synergy among speech know-how and bio-inspired smooth computing tools. via useful instances, the writer explores, dissects and examines how smooth computing may well supplement traditional thoughts in speech enhancement and speech attractiveness as a way to supply powerful platforms. the cloth is mainly valuable to graduate scholars and skilled researchers who're attracted to increasing their horizons and investigating new study instructions via evaluate of the theoretical and useful settings of sentimental computing equipment in very fresh speech applications.

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In order to make an adequate comparison with the baseline front-end, after the GA processing, the MFCCs static vectors are expanded to produce a 39-dimensional (static+dynamic) vector. 5 Fig. 3 Fitness variation of the first optimized weights of G matrix with respect to the number of generations within the evolutionary process. 1 show that the use of the KLT-GA as a preprocessing approach to enhance the MFCCs that were used for recognition with 8mixture Gaussian HMMs using tri-phone models, leads to a significant improvement in the accuracy of speech recognition.

Thus, speech recognition is a pattern classification issue. From a probabilistic perspective, the ASR can be formulated using the Bayesian statistical framework. Let w refer to a sequence of phones or words, which produces a sequence of observable acoustic data o, sent through a noisy transmission channel. The recognition process aims to provide the most likely phone sequence w’ given the acoustic data o. 1) where « is the set of all possible phone sequences, p(w) is the prior probability determined by the language model that the speaker utters w, and p(w/o) is the conditional probability that the acoustic channel produces the sequence o.

The relationship between dialog management systems and ASR is also investigated. Finally, a new paradigm giving soft computing techniques a new role in speech interactive systems is presented. 1 Evolution of Speech Recognition Systems Automatic speech recognition has made enormous progress over the last two decades. Advances in both computing devices and algorithm development have facilitated these historical changes. In general, ASR can be viewed as successive transformations of the acoustic micro-structure of the speech signal into its implicit phonetic macro-structure.

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