Abstract:
A system comprising at least one hardware processor and a non-transitory computer- readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive a voice recording comprising a phonation by a subject, analyze said voice recording to calculate a fundamental frequency contour curve of said phonation, measure at least one of (i) a time period from a start of said phonation until said contour curve reaches a settled level, (ii) a slope of said contour curve during said time period, and (iii) an area under said contour curve during said time period, and determine a glottal closure insufficiency in said subject based, at least in part, on said measuring.
Abstract:
A system and method of speech modification may include: receiving a recorded speech, comprising one or more phonemes uttered by a speaker; segmenting the recorded speech to one or more phoneme segments (PS), each representing an uttered phoneme; selecting a phoneme segment (PSk) of the one or more phoneme segments (PS); extracting a portion of the recorded speech, said portion corresponding to a first timeframe (T̃) that comprises the selected phoneme segment; receiving a representation (P͠ * ) of a phoneme of interest P*; and applying a machine learning (ML) model on (a) the extracted portion of the recorded speech and (b) on the representation (P͠ * ) of the phoneme of interest P*, to generate a modified version of the extracted portion of recorded speech, wherein the phoneme of interest (P*) substitutes the selected phoneme segment (PSk).
Abstract:
A computerized method and system of automatically analyzing a Doppler echocardiography (DE) image, the method comprising: obtaining one or more trained weight vectors each corresponding to a polygon type with a fixed number of vertices; obtaining a plurality of candidate polygons belonging to at least one polygon type; extracting a plurality of feature functions from the DE image for each candidate polygon, including vertex related feature functions and edge related feature functions; calculating a score for each candidate polygon using the extracted feature functions and a trained weight vector corresponding to the polygon type with the same number of vertices as the candidate polygon; and determining a candidate polygon with a highest calculated score to be a predicted polygon for the DE image, whereby the predicted polygon represents a pattern enclosed in the DE image.