Med Biol Eng Comput - Pathological speech signal analysis and classification using empirical mode decomposition.

Tópicos

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Resumo

Automated classification of normal and pathological speech signals can provide an objective and accurate mechanism for pathological speech diagnosis, and is an active area of research. A large part of this research is based on analysis of acoustic measures extracted from sustained vowels. However, sustained vowels do not reflect real-world attributes of voice as effectively as continuous speech, which can take into account important attributes of speech such as rapid voice onset and termination, changes in voice frequency and amplitude, and sudden discontinuities in speech. This paper presents a methodology based on empirical mode decomposition (EMD) for classification of continuous normal and pathological speech signals obtained from a well-known database. EMD is used to decompose randomly chosen portions of speech signals into intrinsic mode functions, which are then analyzed to extract meaningful temporal and spectral features, including true instantaneous features which can capture discriminative information in signals hidden at local time-scales. A total of six features are extracted, and a linear classifier is used with the feature vector to classify continuous speech portions obtained from a database consisting of 51 normal and 161 pathological speakers. A classification accuracy of 95.7?% is obtained, thus demonstrating the effectiveness of the methodology.

Resumo Limpo

autom classif normal patholog speech signal can provid object accur mechan patholog speech diagnosi activ area research larg part research base analysi acoust measur extract sustain vowel howev sustain vowel reflect realworld attribut voic effect continu speech can take account import attribut speech rapid voic onset termin chang voic frequenc amplitud sudden discontinu speech paper present methodolog base empir mode decomposit emd classif continu normal patholog speech signal obtain wellknown databas emd use decompos random chosen portion speech signal intrins mode function analyz extract meaning tempor spectral featur includ true instantan featur can captur discrimin inform signal hidden local timescal total six featur extract linear classifi use featur vector classifi continu speech portion obtain databas consist normal patholog speaker classif accuraci obtain thus demonstr effect methodolog

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