Abstract: Disclosed herein is a method for use in detection of abnormal behavior of a group of a plurality of entities of a computer system. The method is arranged to be performed by a processing system and comprises: creating a model of normal behavior of the group of entities; and determining, in accordance with the model of normal behavior of the group of entities, a parameter indicative of abnormal behavior of the group of entities. Also disclosed is an equivalent computer readable medium and anomalous behavior detection system.

Abstract: Disclosed herein is a method for use in detection of abnormal behavior of a group of a plurality of entities of a computer system. The method is arranged to be performed by a processing system and comprises: creating a model of normal behavior of the group of entities; and determining, in accordance with the model of normal behavior of the group of entities, a parameter indicative of abnormal behavior of the group of entities. Also disclosed is an equivalent computer readable medium and anomalous behavior detection system.

Abstract: Examples of processing audio data are described. In certain examples, a transcript language model is based on text data representative of a transcript associated with the audio data. The audio data is processed to determine at least a set of confidence values for language elements in a text output of the processing, wherein the processing uses the transcript language model. The set of confidence values enable a determination to be made. The determination relates to whether the text data is associated with said audio data based on said set of confidence values.

Abstract: A speech recognition system, according to an example embodiment, includes a data storage to store speech training data. A training engine determines consecutive breakout periods in the speech training data, calculates forward and backward probabilities for the breakout periods, and generates a speech recognition Hidden Markov Model (HMM) from the forward and backward probabilities calculated for the breakout periods.

Abstract: A speech recognition system, according to an example embodiment, includes a data storage to store speech training data. A training engine determines consecutive breakout periods in the speech training data, calculates forward and backward probabilities for the breakout periods, and generates a speech recognition Hidden Markov Model (HMM) from the forward and backward probabilities calculated for the breakout periods.