Sociolinguistic Conditioning of Phonetic Category Realisation in Non-Native Speech

The realisation of phonetic categories reflects a complex relationship between individual phonetic parameters and both linguistic and extra-linguistic conditioning of language usage. The present paper investigates the effect of selected socio-linguistic variables, such as the age, the amount of language use and cultural/social distance in English used by Polish immigrants to the U.S. Individual parameters used in the realisation of the category ‘voice’ have been found to vary in their sensitivity to extra-linguistic factors: while the production of target-like values of all parameters is related to the age, it is the closure duration that is most stable in the correspondence to the age and level of language proficiency. The VOT and vowel duration, on the other hand, prove to be more sensitive to the amount of language use and attitudinal factors

Learning Background-Aware Correlation Filters for Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient - and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers including those based on a deep learning paradigm