37 results about "Text normalization" patented technology

Algorithms for synthesizing speech used to identify media assets are provided. Speech may be selectively synthesized form text strings associated with media assets. A text string may be normalized and its native language determined for obtaining a target phoneme for providing human-sounding speech in a language (e.g., dialect or accent) that is familiar to a user. The algorithms may be implemented on a system including several dedicated render engines. The system may be part of a back end coupled to a front end including storage for media assets and associated synthesized speech, and a request processor for receiving and processing requests that result in providing the synthesized speech. The front end may communicate media assets and associated synthesized speech content over a network to host devices coupled to portable electronic devices on which the media assets and synthesized speech are played back.

The present invention enables accurate detection of risks from an electronic mail message. In a mail inspection unit, an information extraction section extracts text and a mail address from electronic mail accumulated in a journal DB, and a text normalization section normalizes the text. A sort-information saving section generates text sort information according to the score obtained from a sorting engine, and stores it in a mail-management-information storage section. A personal-information saving section extracts personal information from a personal-information storage section according to the mail address, and stores it in the mail-management-information storage section. Finally, a risk-level determination section compares the information stored in the mail-management-information storage section with the information stored in a category-information storage section to determine the risk level of the electronic mail.

A system for identifying deception within a text includes a processor for receiving and processing a text file. The processor includes a deception indicator tag analyzer for inserting into the text file at least one deception indicator tag that identifies a potentially deceptive word or phrase within the text file, and an interpreter for interpreting the at least one deception indicator tag to determine a distribution of potentially deceptive word or phrases within the text file and generating deception likelihood data based upon the density or distribution of potentially deceptive word or phrases within the text file. A method for identifying deception within a text includes the steps of receiving a first text to be analyzed, normalizing the first text to produce a normalized text, inserting into the normalized text at least one part-of-speech tag that identifies a part of speech of a word associated with the part-of-speech tag, inserting into the normalized text at least one syntactic label that identifies a linguistic construction of one or more words associated with the syntactic label, inserting into the normalized text at least one deception indicator tag that identifies a potentially deceptive word or phrase within the normalized text, interpreting the at least one deception indicator tag to determine a distribution of potentially deceptive word or phrases within the normalized text, and generating deception likelihood data based upon the density or frequency of distribution of potentially deceptive word or phrases within the normalized text.

A method and apparatus are provided that perform text normalization and inverse text normalization using a single grammar. During text normalization, a finite state transducer identifies a second string of symbols from a first string of symbols it receives. During inverse text normalization, the context free transducer identifies the first string of symbols after receiving the second string of symbols.

The invention discloses a text-to-speech conversion method and device, an electronic device and a storage medium. The method comprises steps that a preset text normalization template matching a to-be-processed text is obtained; text normalization processing of the to-be-processed text is performed according to the matched preset text normalization template to obtain a normalization text; accordingto a word pre-segmentation template corresponding to the preset text normalization template, the word pre-segmentation information is added to the normalization text; the normalization text is segmented according to the word pre-segmentation information and a word segmentation model to obtain a word segmentation text; the word segmentation text is converted to a voice message. The method is advantaged in that word segmentation accuracy is improved, and accuracy of the synthesized speech is guaranteed.

A system and method for providing an adaptive Interaction Logging functionality to help agents reduce the time spent documenting contact center interactions. In a preferred embodiment the system uses a pipeline comprising audio capture of a telephone conversation, automatic speech transcription, text normalization, transcript generation and candidate call log generation based on Real-time and Global Models. The contact center agent edits the candidate call log to create the final call log. The models are updated based on analysis of user feedback in the form of the editing of the candidate call log done by the contact center agents or supervisors. The pipeline yields a candidate call log which the agents can edit in less time than it would take them to generate a call log manually.

The present invention is an iterative method for normalization of a probe image against the Eigenspace learned from a database of images. The invention is also an iterative method for normalizing the n images in a database, wherein the normalization is carried out without using a predetermined criterion.

The invention relates to a single-character text normalization model training method and device, and a single-character text recognition method and device. The model training method comprises the following steps: acquiring a plurality of single-character sample pictures; normalizing the single-character sample pictures to obtain standard character pictures corresponding to the single-character sample pictures; generating a training data set according to the plurality of single-character sample pictures and standard character pictures in one-to-one correspondence with the plurality of single-character sample pictures; and training a deep learning neural network by using the training data set and a mean square loss function to obtain a single-character text normalization model. The trainingdata set used in training is composed of original data and the standard character pictures which are obtained through normalization processing and have a unified style, so that in the process of training the model, the training and convergence of the model can be accelerated, the model can better learn the essential characteristics of various input texts, and the recognition precision of the modelis further improved.

The application provides a text normalization method, device and apparatus and a readable storage medium. The method includes obtaining the text to be normalized; processing the text content of the text to be regularized into a plurality of text units to obtain a preprocessed text, wherein one text unit in the preprocessed text is a word or a word; based on the normalized category information corresponding to each text unit in the preprocessed text, normalizing the text units to be normalized in the text to be normalized to obtain the normalized text. The text regularization method provided bythe present application can regularize the numeral-related Chinese characters into Arabic numerals or special symbols, thereby obtaining text data that is convenient for users to read and understand.The text regularization method provided by the present application is easy to realize and has good regularization effect.

The present invention relates to a reliable search method base on content trust, comprising the following steps: A, receiving search keywords of users through a user interaction module, and distributing the search keywords to each search engine providing original search service; B, receiving traditional search results provided by each search engine, and submitting the traditional search results to a content trust detection module; C, performing the operations such as the elimination of repetition, the text normalization, the comprehension of trust semantemes, the calculation of content credibility and the rearrangement of search results, submitting the reliable search results to the user interaction module; D, presenting the reliable search results through the user interaction module to the users. The reliability of the essence of text information, text content is evaluated in the invention, And three text-content-trust assessment methods are proposed base on trust facts, trust evidences and trust characteristics, and are unified by utilizing Bayes network. The credibility of the text contents is applied to a sorting algorithm, which can improve the precision of the search results.