699 results about "Frequency of occurrence" patented technology

Input text data undergoes language analysis to generate prosody, and a speech database is searched for a synthesis unit on the basis of the prosody. A modification distortion of the found synthesis unit, and concatenation distortions upon connecting that synthesis unit to those in the preceding phoneme are computed, and a distortion determination unit weights the modification and concatenation distortions to determine the total distortion. An Nbest determination unit obtains N best paths that can minimize the distortion using the A* search algorithm, and a registration unit determination unit selects a synthesis unit to be registered in a synthesis unit inventory on the basis of the N best paths in the order of frequencies of occurrence, and registers it in the synthesis unit inventory.

A method for selecting an emphasis image from a collection of images based on facial identification comprises the steps of: (a) obtaining a collection of digital images; (b) detecting image patterns indicative of the presence of one or more faces in the digital images, thereby identifying one or more detected faces for each image in which a face is detected; (c) recognizing one or more faces from the detected faces for each of the images in which a face is detected; and (d) scoring an image based on the relative frequency of occurrence of a recognized face within the collection of images, thereby producing an emphasis image characteristic of the most frequently occurring face in the collection of images.

A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents as represented by their corresponding vectors in said vector space, so that said each subspace corresponds to a class.

A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space. The similarities of those documents can be ascertained from the position of their respective pseudo-document vectors in the representative semantic space.

An information retrieval system includes a query revision architecture that integrates multiple different query revisers, each implementing one or more query revision strategies. A revision server receives a user's query, and interfaces with the various query revisers, each of which generates one or more potential revised queries. The revision server evaluates the potential revised queries, and selects one or more of them to provide to the user. A session-based reviser suggests one or more revised queries, given a first query, by calculating an expected utility for the revised query. The expected utility is calculated as the product of a frequency of occurrence of the query pair and an increase in quality of the revised query over the first query.