Search Results - (Author, Cooperation:S. Turajlic)

2016-04-29

American Association for the Advancement of Science (AAAS)

0036-8075

1095-9203

Biology

Chemistry and Pharmacology

Computer Science

Medicine

Natural Sciences in General

Physics

Clone Cells/pathology ; *Evolution, Molecular ; Genetic Variation ; Humans ; Neoplasm Metastasis/*genetics/*pathology ; Neoplasms/classification/genetics/pathology ; Phylogeny

1531-5878

Springer Online Journal Archives 1860-2000

Electrical Engineering, Measurement and Control Technology

Abstract Successful speech recognition is highly dependent on appropriate speech segmentation. The poor efficiency of the sequential detection of abrupt changes in the signals with relatively short stationary intervals, as is the case with speech signals, can be improved by the off-line maximum likelihood segmentation algorithm. In this paper the new segmentation algorithm is presented. For the a priori known number of segments, the algorithm determines such signal partitions for which the sum of segment distortion is minimal. The generalized maximum likelihood distortion measure has been introduced, and has proven to be particularly efficient on short signal segments. In the case of an unknown number of segments, its estimate is obtained comparing the reduction of the distortion. The asymptotic properties of the distortion sequence have been analyzed, which led to the definition of the presented segmentation algorithm. The introduced measure can be applied both to the AR and ARMA models. The segmentation algorithm is verified on test signals as well as on the natural speech signal, for which the pitch synchronous framing scheme is applied. The experimental results also include a comparison of the AR and ARMA model-based segmentations. The first results show that ARMA model-based segmentation gives somewhat better results than the AR model algorithm.

Electronic Resource