Abstract: Exemplary system and methods use a combination of application modules and neural network architecture for multi-speaker and multi-language speech analysis. The exemplary system can receive a natural language input, which it decomposes into plural segments. A sub-group of the plural segments are accumulated in a buffer where each segment representing a period during which voice activity is detected. The sub-groups are analyzed for voice activity of multiple speakers and one or more text segments are generated based on the speakers. A semantic vector for each text segment is generated and stored in vector memory. Relevant data associated with each semantic vector is retrieved from the vector memory based on a similarity measure; and a response including specified information extracted from the one or more text segments is generated based on at least the relevant data.

Abstract: A segmentation method is provided for the automated segmentation of spot-light structures into D images allowing precise quantification and classification of said structures and said images, based on a plurality of criteria, and further allowing the automated identification of multi-spot based patterns present in one or a plurality of images. In a preferred embodiment, the invention is used for the analysis of 2D gel electrophoresis images, with objective of quantifying protein expressions and for allowing sophisticated multi-protein pattern based image data mining, as well as image matching, registration, and automated classification.