A continuous speech recognition system integrating additional acoustic knowledge sources in a data-driven beam search algorithm

The paper presents a continuous speech recognition system which integrates an additional acoustic knowledge source into the data-driven beam search algorithm. Details of the object oriented implementation of the beam search algorithm will be given. Integration of additional knowledge sources is treated within the flexible framework of Dempster-Shafer theory. As a first example, a rule-based plosive detector is added to the baseline system

Fourth European Conference on Infections in Leukaemia (ECIL-4): Guidelines for Diagnosis and Treatment of Human Respiratory Syncytial Virus, Parainfluenza Virus, Metapneumovirus, Rhinovirus, and Coronavirus

Respiratory viruses have been recognized as a significant cause of morbidity and mortality in patients with leukemia and those undergoing hematopoietic stem cell transplantation. The risk for lower respiratory tract infections and a fatal outcome appears to depend on the intrinsic virulence of the specific community-acquired respiratory virus as well as factors specific to the patient, the underlying disease, and its treatmen

A language-independent, openvocabulary system based on HMMs for recognition of ultra low resolution words

ABSTRACT In this paper, we introduce and evaluate a system capable of recognizing ultra low resolution words extracted from images such as those frequently embedded on web pages. The design of the system has been driven by the following constraints. First, the system has to recognize small font sizes where antialiasing and resampling procedures have been applied. Such procedures add noise on the patterns and complicate any a priori segmentation of the characters. Second, the system has to be able to recognize any words in an open vocabulary setting, potentially mixing different languages. Finally, the training procedure must be automatic, i.e. without requesting to extract, segment and label manually a large set of data. These constraints led us to an architecture based on ergodic HMMs where states are associated to the characters. We also introduce several improvements of the performance increasing the order of the emission probability estimators and including minimum and maximum duration constraints on the character models. The proposed system is evaluated on different font sizes and families, showing good robustness for sizes down to 6 points

A language-independent, openvocabulary system based on HMMs for recognition of ultra low resolution words

Abstract: In this paper, we introduce and evaluate a system capable of recognizing words extracted from ultra low resolution images such as those frequently embedded on web pages. The design of the system has been driven by the following constraints. First, the system has to recognize small font sizes between 6-12 points where anti-aliasing and resampling filters are applied. Such procedures add noise between adjacent characters in the words and complicate any a priori segmentation of the characters. Second, the system has to be able to recognize any words in an open vocabulary setting, potentially mixing different languages in Latin alphabet. Finally, the training procedure must be automatic, i.e. without requesting to extract, segment and label manually a large set of data. These constraints led us to an architecture based on ergodic HMMs where states are associated to the characters. We also introduce several improvements of the performance increasing the order of the emission probability estimators, including minimum and maximum width constraints on the character models and a training set consisting all possible adjacency cases of Latin characters. The proposed system is evaluated on different font sizes and families, showing good robustness for sizes down to 6 points

Small interfering RNA targeting HIF-1α reduces hypoxia-dependent transcription and radiosensitizes hypoxic HT 1080 human fibrosarcoma cells in vitro

Background: : Hypoxia inducible factor-1 has been identified as a potential target to overcome hypoxia-induced radioresistance The aim of the present study was to investigate whether selective HIF-1 inhibition via small interfering RNA (siRNA) targeting hypoxia-inducible factor 1α (HIF-1α) affects hypoxia-induced radioresistance in HT 1080 human fibrosarcoma cells. Material and Methods: : HIF-1α expression in HT 1080 human fibrosarcoma cells in vitro was silenced using HIF-1α siRNA sequence primers. Quantitative real-time polymerase chain reaction assay was performed to quantify the mRNA expression of HIF-1α. HIF-1α protein levels were studied by Western blotting at 20% (air) or after 12 hours at 0.1% O2 (hypoxia). Cells were assayed for clonogenic survival after irradiation with 2, 5, or 10 Gy, under normoxic or hypoxic conditions in the presence of HIF-1α-targeted or control siRNA sequences. A modified oxygen enhancement ratio (OER´) was calculated as the ratio of the doses to achieve the same survival at 0.1% O2 as at ambient oxygen tensions. OER´ was obtained at cell survival levels of 50%, 37%, and 10%. Results: : HIF-1α-targeted siRNA enhanced radiation treatment efficacy under severely hypoxic conditions compared to tumor cells treated with scrambled control siRNA. OER was reduced on all survival levels after treatment with HIF-1α-targeted siRNA, suggesting that inhibition of HIF-1 activation by using HIF-1α-targeted siRNA increases radiosensitivity of hypoxic tumor cells in vitro. Conclusion: : Inhibition of HIF-1 activation by using HIF-1α-targeted siRNA clearly acts synergistically with radiotherapy and increase radiosensitivity of hypoxic cells in vitr