19 results about "Correction text" patented technology

According to the voice transliteration text error correction method and device, the storage medium and the computer equipment, after the voice transliteration text is received in real time, the text is subjected to primary error detection, and the first error confidence coefficient is obtained; when the first error confidence coefficient is smaller than a fast error correction threshold value, the voice transcription text is directly output, and unnecessary error correction is avoided; otherwise, performing word-level rapid error correction on the voice transcription text to obtain a rapid error correction text, and performing secondary error detection on the rapid error correction text to obtain a second error confidence coefficient. Judging whether the second error confidence is smaller than a depth error correction threshold value or not; if yes, the rapid error correction text is directly output, and if not, semantic-level deep error correction is conducted on the rapid error correction text through a deep error correction model, and a deep error correction text is obtained and then output. Through a hierarchical error correction processing strategy, the method can be adapted to different service scenes, and meanwhile, dynamic balance between quality and efficiency of different service scenes can be realized by adjusting two error correction thresholds.

The invention discloses a text information error correction model training system based on deep learning, and belongs to the technical field of text information processing. The system comprises a data input and preprocessing module which receives an original text and a correct text corresponding to the original text and processes the original text and the correct text into a wrong text, a standard correct text and a wrong correct text training pair; the model reasoning module receives the error text and outputs a corrected text; the result evaluation module analyzes the corrected text and the standard correct text, identifies error types and calculates quantitative indexes; the reinforcement learning signal generation module constructs reinforcement learning reward signals based on the error types and the quantitative indexes; the model optimization module updates model parameters based on the reinforcement learning reward signal and carries out training to obtain a new text information error correction model; and the model deployment module deploys the new text information error correction model as RESTful API service. The method improves the accuracy and efficiency of text error correction through the reinforcement learning optimization model, and is suitable for various scenes needing text error correction processing.

The application discloses a Chinese error correction method and system based on multi-agent cooperation and retrieval enhancement, and belongs to the technical field of network space security. The method comprises the following steps: 1, based on a large language model and an agent prompt engineering, a plurality of Chinese error correction expert agents and one Chinese error correction judge agent are constructed; 2, an error correction text knowledge base is built, corpus in the knowledge base is converted into a vector database, the Chinese error correction expert agent and the judge agent are accessed, and are used for information retrieval of the Chinese error correction expert agent and the judge agent, so that the context understanding capability is enhanced, and a retrieval enhancement generation function is realized; 3, detection prompts, discrimination prompts and feedback prompts are designed, a multi-agent closed-loop feedback module is constructed, information cooperation of each agent, step-by-step reasoning, result verification and iterative optimization are realized, and a Chinese error correction task is completed. The method can realize generalization error correction under the condition that the training data is limited, and improve the adaptability of the model to unknown errors.

The invention provides a real-time dynamic display transcription method and system, electronic equipment and a storage medium. The method comprises the following steps: segmenting a single-channel voice stream into continuous multi-frame voice streams; performing human voice detection on the voice stream to obtain a detection result; if a preset number of continuous target voice streams exist, streaming transcription is carried out on each frame of target voice stream and the acquired human voice stream in real time by using a current language through a streaming transcription model; in the streaming transcription process, accumulated audios are collected every preset time period, and the streaming text in the preset time period is corrected by utilizing an accumulated text obtained by transcription of the accumulated audios until a human voice ending signal is detected; correcting the streaming text in the target time period by using a target accumulated text obtained by transferring the target accumulated audio in the target time period to obtain a target corrected text; decoding the target correction text to obtain and display a final text; and updating the current language by using a language obtained by performing language identification on the target accumulated audio.

The invention discloses a model-based text processing method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a training text, and obtaining a reference text and reference phonetic notation of the training text, the reference text being obtained by carrying out text error correction processing on the training text, the reference phonetic notation is obtained by carrying out phonetic notation processing on the reference text; calling a task model to carry out text error correction processing on the training text to obtain a prediction error correction text of the training text; calling the task model to carry out phonetic notation prediction processing on the training text to obtain predicted phonetic notation of the training text; on the basis of the difference between the reference text and the predicted error correction text and the difference between the reference phonetic notation and the predicted phonetic notation, a task model is optimized, and the optimized task model is used for processing a text error correction task. By adopting the method and the device, the text error correction accuracy can be improved and the text error correction effect can be optimized in the text error correction task.

The invention relates to a multi-stage spelling error correction method and device for a medical image report and a medium, in a non-term error correction stage, for each text segment, a pinyin error correction knowledge base and a polyphone probability base are called, screening and replacement are carried out according to text similarity and editing distance, candidate correction for general spelling errors is generated, and the spelling error correction efficiency is improved. Obtaining a text fragment after non-term error correction; in the term error correction stage, a medical term package is called, candidate correction for term spelling errors is generated and screened through candidate interval positioning, set similarity screening and sequence similarity calculation, and text segments after term error correction are obtained; and enabling the text segments after non-term error correction and the text segments after term error correction to correspond to each other at the text position, correcting the candidates with overlapping or conflicting, and performing resolution and combination according to a preset priority rule to obtain a final error correction result. Compared with the prior art, the method has the advantages of wide range, high accuracy, high reliability and the like.

The invention discloses a real-time discourse correction method, a related device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current recognition text of a real-time audio stream, and obtaining a plurality of historical recognition texts subjected to text correction before the current recognition text and historical correction texts obtained through text correction; based on at least one of the current recognition text and the historical correction text, retrieving in the scene knowledge graph to obtain a reference knowledge text related to the execution of the text correction; wherein the scene knowledge graph is a reference knowledge graph related to an interaction scene where the real-time audio stream is located; and performing text correction on the current recognition text based on the historical correction text and the reference knowledge text to obtain a current correction text of the current recognition text. According to the scheme, the accuracy of real-time discourse correction can be improved, especially when the real-time discourse relates to terminologies or specific domain knowledge.

The invention provides a speech recognition error correction method and device, and relates to the technical field of data processing, and the method comprises the steps: inputting a current audio frame in a to-be-recognized user audio into a speech recognition model, and obtaining the acoustic features and first text characters of the current audio frame; inputting the acoustic feature and the first text character of the current audio frame and the historical correction text sequence into a speech recognition error correction model to obtain a correction text character corresponding to the current audio frame; wherein the historical corrected text sequence is a corrected text sequence obtained after historical audio frames, before the current audio frame, in the audio of the user to be recognized sequentially pass through a voice recognition model and a voice recognition error correction model; the speech recognition error correction model is obtained by training based on a training sample carrying a forced alignment text label, and the forced alignment text label is obtained by performing forced alignment on a real text label corresponding to the training sample and a speech sample signal corresponding to the training sample.

This invention relates to the field of data processing technology and discloses a multilingual voice data processing method for real-time intercom communication of VHF equipment at sea. This method addresses the problem that during voice data processing, intercom content may be presented in a simplified manner rather than with complete semantics. The method includes: determining the context dependency of the current intercom round corresponding to the correction text; when the correction text is determined to have a context dependency, determining semantic missingness to obtain a semantic missingness determination result; when the semantic missingness determination result indicates that the correction text has semantic missingness, extracting context text associated with the current intercom round from historical intercom text, and completing the missing communication object, location description, risk background, or operation object in the correction text based on the context text to obtain the final completed text. This effectively improves the accuracy, continuity, and practical reliability of multilingual voice data processing at sea.

The application belongs to the technical field of natural language processing, and particularly relates to a disorderly text correction method based on a pointer network; the method comprises the following steps: obtaining correction text data and constructing labels; extracting a word-level feature vector of the correction text data; extracting a local feature vector according to the word-level feature vector of the correction text; splicing the word-level feature vector and the local feature vector to construct a fusion feature vector; constructing a span pointer score table and a next pointer score table by using a query mechanism according to the fusion feature vector; narrowing the next pointer score table according to the span pointer score table; reordering the disorderly text according to the span score table and the next pointer score table to obtain a final corrected text; calculating a total loss of a model and adjusting model parameters according to the total loss to obtain a trained disorderly text correction model; and using the trained model to correct the text; the application improves the accuracy of an automatic proofreading system and the model reasoning speed is faster.

The invention discloses a text processing method, a related device, computer equipment and a storage medium. The method provided by the invention comprises the steps of obtaining a global correction text corresponding to a to-be-processed text, wherein the global correction text is obtained after correction; acquiring a local understanding text corresponding to the to-be-processed text; constructing a first prompt text according to the global correction text, the local understanding text and a first prompt word template, wherein the first prompt word template is used for guiding a text correction model to correct the text; on the basis of the first prompt text, a local correction text for the local understanding text is obtained through a text correction model, and the local correction text is obtained after the text correction model corrects the local understanding text on the basis of the global correction text. According to the method and the device, the text understanding accuracy of the model can be improved, and the local understanding text does not need to be manually corrected, so that the labor cost is saved, and the text correction efficiency is improved.

The invention discloses a text error correction method and device, a model training method and device, equipment and a storage medium, and belongs to the technical field of electronic equipment. The text error correction method comprises the following steps: performing voice recognition on received first voice data to obtain a first text; a first error correction text of the first text is obtained on the basis of first information and the first text, the first information is visual information obtained on the basis of second information, and the second information comprises at least one of the following items: content displayed by a display screen when first voice data is received; receiving an image collected by a camera when the first voice data is received; and outputting reply information corresponding to the first voice data based on the first error correction text.

The application provides a kind of picture-text conversion correction method, equipment and storage medium, it is related to image processing field.The method comprises: obtaining the original image to be processed and inputting original image into the preset graph text model to generate text description;Based on original image, text description and preset text graph model, obtain simulated image;Original image and simulated image are analyzed to obtain image slice pair and the similarity data of each image slice pair;According to similarity data, the image slice pair is analyzed to generate correction text;The correction text is corrected to obtain target text by text description.Through the way of original image generating text description, text description generating simulated image, original image and simulated image are compared to correct text description, effectively reduce the object illusion in text description and the situation that text description and picture content are inconsistent, improve the accuracy of text description generated by picture-text conversion.

The embodiment of the invention provides a voice generation method and device, computer equipment, a computer readable storage medium and a computer program product, and belongs to the field of multimedia. The voice generation method comprises the following steps: acquiring a video clip and a target text obtained by voice recognition based on the video clip; taking the target text and the video clip as model input, and outputting a target correction text through a pre-trained target visual language model; obtaining a plurality of translation statements corresponding to the target language according to the target correction text, and determining a target translation text from the plurality of translation statements according to the phoneme number of the target correction text and the phoneme numbers of the plurality of translation statements; and according to the target translation text, generating a target voice corresponding to the target language. According to the embodiment of the invention, the video clip and the voice recognition text corresponding to the video clip are input into the pre-trained target visual language model to obtain the corrected text, so that the accuracy of the voice recognition text is improved, and the accuracy of translation and voice synthesis is improved.

The application belongs to the technical field of large language model optimization, and discloses a large data driving-based large language model optimization method and system. The method comprises the following steps: inputting public text data into a test model to obtain test reply data, and calculating the test deviation between the test reply data and the reply data; when the test deviation meets a preset correction trigger condition, corresponding correction text data and correction reply data are identified; data supplement processing is performed based on the correction text data and the correction reply data to generate supplement data for model optimization; the data supplement processing comprises similarity comparison of the correction text data to divide the correction text data into data groups, generation of error registration data, and performance of a supplement operation based on a statistical analysis result. Through inductive analysis of the deviation and quality control of the supplement data, the application improves the reply accuracy, logical consistency and robustness of the target language model.

The invention relates to the technical field of computer information processing, provides a text error correction method and system fusing a domain knowledge graph and a deep learning model, and is used for solving the problems of low accuracy and poor normalization of government affair document digital processing in the prior art. The method comprises the steps of obtaining a target scanning image and historical official document data; based on the historical official document data, establishing a government affair domain knowledge graph; performing image processing on the target scanning image to obtain character change information, and generating topology correction data based on the character change information; performing multi-scale fusion on the topology correction data and the target scanning image to generate character structure information, and associating the character structure information with the government affair domain knowledge graph to generate error positioning information; and generating an error correction text sequence based on the error positioning information, and performing multi-round iterative optimization on the error correction text sequence through a discriminator network to generate a final text. According to the invention, the accuracy and normalization of digital processing of government affair documents are improved.

The present application relates to a kind of laryngectomy post-voice reconstruction method and system based on hidden space editing, low-frequency body surface vibration signal and high-frequency air-conducted acoustic signal produced simultaneously when patient vocalization is carried out, and dual-channel heterogeneous signal acquisition and fusion are carried out;Based on soft feature mapping and hidden space editing, the signal after fusion is reconstructed to output correction text;Based on reference audio and correction text, personalized waveform file is synthesized and played back.By retaining soft features and intervening in hidden space, the probability distribution is retained, the decision-making power is moved backward, real-time error correction during inference can be achieved, and high-fidelity intent reconstruction can be achieved.Through dual-channel sampling, the low-frequency body surface vibration signal and high-frequency air-conducted acoustic signal collected are complementary in frequency domain, and the complete acoustic information is reconstructed by combining them, which improves the noise immunity.Based on reference audio, personalized speech files can be synthesized without samples, noise immunity and personalization can coexist, and important technical support is provided for the development of intelligent medical treatment.

A question-and-answer method and apparatus are disclosed to improve the accuracy of memory retrieval. The method includes: an electronic device acquiring a target question text input by a user; the electronic device retrieving a first historical question text with the highest semantic similarity to the target question text from at least one set of stored historical dialogue records, and outputting a first response text corresponding to the first historical question text; wherein each set of historical dialogue records contains historical question text and corresponding response text; the electronic device acquiring at least one target keyword of the target question text, and acquiring keywords corresponding to at least one set of historical dialogue records; the electronic device determining a target historical dialogue record with the highest similarity between the corresponding keyword and at least one target keyword, and acquiring a second response text from the target historical dialogue record; when the first response text and the second response text are inconsistent, the electronic device outputting a correction text based on the second response text.

The invention discloses a text generation method and device, equipment, a storage medium and a program product. The text generation method comprises the steps of obtaining a first candidate text based on a first task prompt word and first role information; based on first task information, correcting the first candidate text to obtain a first corrected text; determining a second task cue word based on the first correction text and the first task cue word; and obtaining a first text based on the second task cue word and the first role information.