10 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 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 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.

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 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 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.