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Intonation has many perceptually significant functions in language that contribute to speech recognition. This study aims to investigate whether intonation cues affect the unmasking of Mandarin Chinese speech in the presence of interfering sounds. Specifically, intelligibility of multi-tone Mandarin Chinese sentences with maskers consisting of either two-talker speech or steady-state noise was measured in three (flattened, typical, and exaggerated) intonation conditions. Different from most of the previous studies, the present study only manipulate and modify the intonation information but preserve tone information. The results showed that recognition of the final keywords in multi-tone Mandarin Chinese sentences was much better under the original F0 contour condition than the decreased F0 contour or exaggerated F0 contour conditions whenever there was a noise or speech masker, and an exaggerated F0 contour reduced the intelligibility of Mandarin Chinese more under the speech masker condition than that under the noise masker condition. These results suggested that speech in a tone language (Mandarin Chinese) is harder to understand when the intonation is unnatural, even if the tone information is preserved, and an unnatural intonation contour decreases releasing Mandarin Chinese speech from masking, especially in a multi-person talking environment. [ABSTRACT FROM AUTHOR]

In order to retrieve similar trademarks from large-scale trademark databases, combining the characteristics of trademark images, this paper presents a trademark image retrieval method based on regional and border feature fusion. Based on the target image extraction, the proposed approach describes the target region and border features. The region feature description is mainly based on the concept of partition block statistics. The region is divided into equal-area unit using concentric circles, and feature extraction is performed in each small block unit. For the border feature description, this study first detect corners, and then construct a Delaunay graph and extract features by combining the corner detected and the Delaunay triangulation reconstruction. In the search process, the method also incorporates information such as the trademark's color characteristics, trademark classification, and trademark keywords. The present study carried out image retrieval experiment on CE-SHAPE-1 database containing 1400 MPEG-7 core experimental shape, a classification trademark database containing 2000 images, and a national trademark database containing approximately 4.89 million images. The experimental results show that the proposed approach combines the advantages of region and border feature description, and can choose the best among various local optimizations, which makes the retrieval result more effective, more in line with human visual perception, and improves the retrieval accuracy. [ABSTRACT FROM AUTHOR]

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson’s disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher’s linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified. [ABSTRACT FROM AUTHOR]