1048 results about "Confidence score" patented technology

The invention relates to a system and method for processing natural language text utilizing disambiguation components to identify a disambiguated sense for the text. For the method, it comprises applying a selection of the components to the text to identify a local disambiguated sense for the text. Each component provides a local disambiguated sense of the text with a confidence score and a probability score. The disambiguated sense is determined utilizing a selection of local disambiguated senses. The invention also relates to a system and method for generating sense-tagged text. For the method, it comprises steps of: disambiguating a quantity of documents utilizing a disambiguation component; generating a confidence score and a probability score for a sense identified for a word provided by the component; if the confidence score for the sense for the word is below a set threshold, the sense is ignored; and if the confidence score for the sense for the word is above the set threshold, the sense is added to the sense-tagged text.

A speech recognition system recognizes an input speech signal by using a first speech recognizer and a second speech recognizer each coupled to a decision module. Each of the first and second speech recognizers outputs first and second recognized speech texts and first and second associated confidence scores, respectively, and the decision module selects either the first or the second speech text depending upon which of the first or second confidence score is higher. The decision module may also adjust the first and second confidence scores to generate first and second adjusted confidence scores, respectively, and select either the first or second speech text depending upon which of the first or second adjusted confidence scores is higher. The first and second confidence scores may be adjusted based upon the location of a speaker, the identity or accent of the speaker, the context of the speech, and the like.

A domain-based speech recognition method and apparatus, the method including: performing speech recognition by using a first language model and generating a first recognition result including a plurality of first recognition sentences; selecting a plurality of candidate domains, by using a word included in each of the first recognition sentences and having a confidence score equal to or higher than a predetermined threshold, as a domain keyword; performing speech recognition with the first recognition result, by using an acoustic model specific to each of the candidate domains and a second language model and generating a plurality of second recognition sentences; and selecting at least one or more final recognition sentence from the first recognition sentences and the second recognition sentences. According to this method and apparatus, the effect of a domain extraction error by misrecognition of a word on selection of a final recognition result can be minimized.

An enhanced system for speech interpretation is provided. The system may include receiving a user verbalization and generating one or more preliminary interpretations of the verbalization by identifying one or more phonemes in the verbalization. An acoustic grammar may be used to map the phonemes to syllables or words, and the acoustic grammar may include one or more linking elements to reduce a search space associated with the grammar. The preliminary interpretations may be subject to various post-processing techniques to sharpen accuracy of the preliminary interpretation. A heuristic model may assign weights to various parameters based on a context, a user profile, or other domain knowledge. A probable interpretation may be identified based on a confidence score for each of a set of candidate interpretations generated by the heuristic model. The model may be augmented or updated based on various information associated with the interpretation of the verbalization.

A method of operation of a navigation system includes: generating a training data from a randomly sampled uncategorized point of interest; generating a trained classifier model by training a classifier model using the training data; generating a category identifier, a confidence score, or a combination thereof for an uncategorized point of interest using the trained classifier model; generating a categorized point of interest by assigning the category identifier to the uncategorized point of interest; calculating a weighted confidence score based on a weighted F-measure for the category identifier, a pair of the category identifier and the confidence score; and consolidating the categorized point of interest based on the weighted confidence score for the category identifier being meeting or exceeding a threshold for displaying on a device.

A user adaptive speech recognition method and apparatus is disclosed that controls user confirmation of a recognition candidate using a new threshold value adapted to a user. The user adaptive speech recognition method includes calculating a confidence score of a recognition candidate according to the result of speech recognition, setting a new threshold value adapted to the user based on a result of user confirmation of the recognition candidate and the confidence score of the recognition candidate, and outputting a corresponding recognition candidate as a result of the speech recognition if the calculated confidence score is higher than the new threshold value. Thus, the need for user confirmation of the result of speech recognition is reduced and the probability of speech recognition success is increased.

An authentication challenge system for performing secondary authentication for an account associated with an online store is described. In one embodiment, the authentication challenge system includes a question generation engine, which can derive a series of questions based upon activity associated with a user account of an online store; a network interface, which can transport the series of one or more questions derived by the question generation engine to authenticate the user to the online store; a confidence engine, which can determine a required confidence level for a successful authentication, and can compute a confidence score of the user identity; and a quality engine, which can adjust the question generation engine and the confidence engine based upon an analysis of question and answer metrics across multiple accounts of the online store. The online store can include digital media, such as music, movies, books or applications for electronic computing devices.

When positional information in an operation window is input (designated), an interpretation result for an input position and a confidence score of the interpretation result are calculated on the basis of the input position information. Assume that a plurality of candidates are present as interpretation results. In presenting the candidates as the interpretation results in this case, the display contents are so changed as to have an enlarged image of a range within which the candidates as the interpretation results fall or the candidates are output using a changed output modality.

A system, method and computer program product for automated voice recognition using the barge-in time of a user's utterance is provided. A list of items is audibly provided to a user. The user selects one item from the list by providing an utterance representing the user's selection. The utterance time (i.e., the barge-in time) is calculated from the utterance of the user. Then, the utterance is compared to each item in the list so as to generate a confidence score for each item in the list, with the confidence score being based on the similarity between the item and the utterance. The confidence scores are also based on the barge-in time. One of the items from the list is selected based on the confidence scores of the items.

A system and method for detecting and tracking an object is disclosed. A camera captures a video sequence comprised of a plurality of image frames. A processor receives the video sequence and analyzes each image frame to determine if an object is detected. The processor applies one or more classifiers to an object in each image frame and computes a confidence score based on the application of the one or more classifiers to the object. A database stores the one or more classifiers and vehicle training samples. A display displays the video sequence.

A method of speech recognition is provided for use in searching a knowledge database. A spoken command is communicated to a system. The spoken command is responded to with a set comprising a plurality of keywords. The plurality of keywords is arranged in a best possible set of matches which set of matches is derived by mathematically combining a speech recognition confidence score and a keyword weighting score. The best possible set of matches is then provided to the user.

A method for performing speech recognition can include receiving user speech and determining a plurality of potential candidates. Each of the candidates can provide a textual interpretation of the speech. Confidence scores can be calculated for the candidates. The confidence scores can be compared to a predetermined threshold. Also, selected ones of the plurality of candidates can be presented to the user as alternative interpretations of the speech when none of the confidence scores is greater than the predetermined threshold. The selected ones of the plurality of candidates can have confidence scores above a predetermined minimum threshold, and thus can have confidence scores within a predetermined range.

Methods of detecting an object in an image using a convolutional neural-network-based architecture that processes multiple feature maps of differing scales from differing convolution layers within a convolutional network to create a regional-proposal bounding box. The bounding box is projected back to the feature maps of the individual convolution layers to obtain a set of regions of interest (ROIs) and a corresponding set of context regions that provide additional context for the ROIs. These ROIs and context regions are processed to create a confidence score representing a confidence that the object detected in the bounding box is the desired object. These processes allow the method to utilize deep features encoded in both the global and the local representation for object regions, allowing the method to robustly deal with challenges in the problem of object detection. Software for executing the disclosed methods within an object-detection system is also disclosed.

Aspects discussed herein present a solution for utilizing large-scale knowledge graphs for inference at scale and generating explanations for the conclusions. In some embodiments, aspects discussed herein learn inference paths from a knowledge graph and determine a confidence score for each inference path. Aspects discussed herein may apply the inference paths to the knowledge graph to improve database lookup, keyword searches, inferences, etc. Aspects discussed herein may generate a natural language explanation for each conclusion or result from one or more inference paths that led to that conclusion or result. Aspects discussed herein may present the best conclusions or results to the user based on selection strategies. The presented results or conclusions may include generated natural language explanations rather than links to documents with word occurrences highlighted.

The present teaching relates to facilitating a guided dialog with a user. In one example, an input utterance is obtained from the user. One or more task sets are estimated based on the input utterance. Each of the one or more task sets includes a plurality of tasks estimated to be requested by the user via the input utterance and is associated with a confidence score computed based on statistics with respect to the plurality of tasks in the task set. At least one of the one or more task sets is selected based on their respective confidence scores. A response is generated based on the tasks in the selected at least one task set. The response is provided to the user.

According to one aspect, a computerized method and apparatus for generating and presenting search snippets that enable user-directed navigation of the underlying audio/video content. The method involves obtaining metadata associated with discrete media content that satisfies a search query. The metadata identifies a number of content segments and corresponding timing information derived from the underlying media content using one or more automated media processing techniques. Using the timing information identified in the metadata, a search result or “snippet” can be generated that enables a user to arbitrarily select and commence playback of the underlying media content at any of the individual content segments.

A validation database (VDB) is built from geographic reference data sources such as MSAG and USPS using automatic address correlation, and links between the records are stored in the VDB. The automatic address correlation employs multiple correlation algorithms, and a score is assigned to each link representing a confidence level. The score is based on a combination of results from the different algorithms. Links having a score representing a partial or exact match are used for address validation purposes. A management interface allows the VDB agent to edit master records and the links, including the score. A remote subscriber can request validation of a proposed address from the VDB, and may include match criteria with the request. If any matches are found, a validation reply is sent from the VDB to the subscriber with the corresponding MSAG records.

Methods of detecting an object in an image using a convolutional neural network based architecture that processes multiple feature maps of differing scales from differing convolution layers within a convolutional network to create a regional-proposal bounding box. The bounding box is projected back to the feature maps of the individual convolution layers to obtain a set of regions of interest. These regions of interest are then processed to ultimately create a confidence score representing the confidence that the object detected in the bounding box is the desired object. These processes allow the method to utilize deep features encoded in both the global and the local representation for object regions, allowing the method to robustly deal with challenges in the problem of robust object detection. Software for executing the disclosed methods within an object-detection system is also disclosed.

Embodiments of the invention detect fraudulently filed tax returns when a user prepares or submits a subject tax return that relates to a subject taxpayer for filing with a government taxing authority. Embodiments analyze tax information of various sources to determine whether the subject tax return is genuine (i.e., not fraudulent). The invention determines confidence indicators based upon some or all of a source location, a source type, data entry characteristics, refund vehicle characteristics, internal consistencies, external historical consistencies, external lateral consistencies, user authentication methods, and a taxpayer risk level. Based upon these analyses the invention calculates a taxpayer identity confidence score, which may be utilized to allow or deny the filing of the subject tax return.

Techniques are disclosed for detecting security incidents based on low confidence security events. A security management server aggregates a collection of security events received from logs from one or more devices. The security management server evaluates the collection of security events based on a confidence score assigned to each distinct type of security event. Each confidence score indicates a likelihood that a security incident has occurred. The security management server determines, based on the confidence scores, at least one threshold for determining when to report an occurrence of a security incident from the collection of security events. Upon determining that at least one security event of the collection has crossed the at least one threshold, the security management server reports the occurrence of the security incident to an analyst.

The disclosure relates to methods, systems and other embodiments directed to determining an information domain match for a natural language (NL) input (e.g., a spoken utterance), and confirming whether the NL input is correctly matched to the information domain. For example, after receiving an NL input, a first information domain to which the NL input belongs and a feature value set may be determined based on a semantic pattern matching technique. Further, a second information domain to which the NL input belongs, and a corresponding confidence score related to the second information domain may be determined. The second information domain may be determined based on a first statistical classification technique. Based on the determined feature value set and the confidence score related to the second information domain, it may be confirmed whether the NL input correctly belongs to the first information domain, e.g., based on a second statistical classification technique.