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Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like Alexa, Cortana, Hi Alexa!, Whatsup Octavia? etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot Anna and github in I know a developer named Anna who can look into this github issue. Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.

A case of acquired hemophilia is presented. Widespread blisters and then ecchymoses developed in a 79-year-old woman who was severely demented. Laboratory studies revealed specific complete inhibition of factor VIII in the blood. In any elderly patient with a bleeding diathesis and an abnormality in intrinsic coagulation, the presence of inhibitors to factor VIII should be suspected.

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