36 results about "Transliteration" patented technology

The invention provides a data intelligent question and answer ChatBI method and device based on a large model. The method comprises the following steps of 1, constructing a database knowledge vector library; and step 2, question semantic transliteration and retrieval. According to problem transcription and data retrieval supplementation, a large language model is utilized to generate a corresponding visual chart according to the characteristics and problems of data, automatic visualization of any data is achieved, meanwhile, a more detailed and clearer expression mode is provided in a dialogue mode, and the method has the advantages of being high in practicability and easy to popularize. The flexible, intelligent and automatic process is realized, and the trend and relationship of the data are more intuitively displayed for customers. By combining a big language model and a data visualization technology, a proper visual chart is automatically generated according to natural language cue words and data, so that the data analysis and presentation efficiency is greatly improved, the learning cost of a user is reduced, and a personalized data display solution is provided for users in different fields.

The invention relates to a voice recognition processing method, system and device based on a conference scene and a medium, and belongs to the technical field of voice processing. The voice recognition processing method comprises the following steps: acquiring an original conference audio stream collected by a microphone array; performing signal preprocessing on the original conference audio stream collected by the main channel, and outputting a pure voice signal; generating a sound source orientation thermodynamic diagram based on the original conference audio stream; extracting multi-dimensional voiceprint feature vectors from the pure voice signals, performing dynamic grouping, outputting a voice fragment set marked with voiceprint IDs, and generating an initial transcription text; dynamically correcting the initial transliteration text, and outputting a transliteration text stream with an industry term tag; and performing periodic memory enhancement processing on the transliteration text stream, outputting and analyzing a long text, and generating structured conference summary data. According to the invention, the automation level and accuracy of conference voice processing can be improved.

The invention discloses an Internet of Things multi-source heterogeneous data management method based on artificial intelligence, and the method comprises the following steps: collecting voice data and text data in an Internet of Things system, and constructing a voice and text pair; inputting the voice data into an improved Whisper model, and outputting an enhanced transliteration text; inputting the enhanced transliteration text and the text data into a semantic encoder based on dynamic weighting to generate a fused semantic embedding vector; calculating semantic similarity between the fused semantic embedding vectors, and screening semantic consistent sample pairs; performing event aggregation on the semantically consistent sample pairs; performing semantic level coding and confidence adaptive fusion processing to generate a unified semantic fusion vector; and constructing a backmarking training set based on expert feedback, and executing periodic increment optimization on the updatable parameters. The voice and text multi-modal data fusion quality and semantic consistency recognition precision are improved, and the method has good expandability and is suitable for intelligent data governance tasks in a complex Internet of Things environment.

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 voice call real-time transcription system and method, and relates to the technical field of computers, and the system comprises a network element module which is used for obtaining corresponding audio data when a call request of a user side is detected; the voice streaming engine is used for carrying out hierarchical compression on the audio data based on a preset perceptual weighted vector quantization algorithm to obtain audio compressed data, and carrying out format conversion processing on the audio compressed data to obtain temporary audio data; the voice engine is used for performing feature extraction on the temporary audio data to obtain multi-modal feature data, and processing the multi-modal feature data based on a preset voice recognition model to obtain text information; and the analysis and optimization module is used for obtaining corresponding real-time transliteration text data according to the text information and a preset vocabulary library based on a preset large model. According to the method, the voice information is comprehensively represented by using the multi-modal feature data, so that the voice recognition model can more accurately convert the voice into the text.

The invention provides a method and device for processing sensitive information in a video, electronic equipment and a storage medium, and relates to the technical field of computer vision, and the method comprises the steps: extracting a voice segment in an audio signal, and determining a transliteration text of the voice segment and a phoneme timestamp of the transliteration text; performing semantic analysis on the transliteration text to obtain first sensitive information in the transliteration text, determining a sensitive voice segment corresponding to the first sensitive information based on the phoneme timestamp, and erasing the sensitive voice segment; and finally, sensitive area identification is carried out on each video frame to obtain second sensitive information in each video frame, and the second sensitive information is erased. According to the method, the sensitive information in the video can be automatically and accurately positioned and efficiently purified, the processed video is ensured to meet the requirements of national laws and regulations, the availability of the video in tasks such as action recognition and cross-modal learning can be remarkably improved, and a pure data basis is provided for high-quality visual model training.

The invention belongs to the technical field of artificial intelligence and medical informatization, particularly relates to a structured electronic medical record generation method based on voice recognition and a large language model, and is particularly suitable for voice-driven electronic medical record automatic generation and structured extraction tasks. The method comprises the following steps: acquiring doctor-patient dialogue audio, and performing multilevel acoustic modeling and semantic coding by using a voice recognition model to obtain a standardized transliteration text; and then, combining the constructed prompt template with historical context information, guiding a large language model to automatically generate a plurality of structured electronic medical record fields, and assisting in outputting diagnosis suggestions. And finally, completing the optimization and confirmation of the structured result through a doctor interaction confirmation mechanism, and generating standardized output meeting the requirements of the electronic medical record system. According to the method, the medical record generation efficiency and the structural quality can be effectively improved, the document burden of doctors is reduced, and good clinical practicability and popularization value are achieved.

The invention provides a Kangba Tibetan speech synthesis front-end modeling method, which can be applied to the technical field of artificial intelligence. The front-end modeling method for voice synthesis of the Conba Tibetan language comprises the following steps: acquiring a Conba Tibetan language text and a corresponding real Tibetan language rhythm tag; based on a preset conbar international phonetic symbol transliteration rule, transliteration is conducted on the conbar Tibetan text to determine a conbar Tibetan phoneme vector; based on the three-layer structure modeling rhythm information of the Kangba Tibetan phoneme vector and the hierarchy-sensitive local self-attention mechanism, generating a prediction rhythm tag; and optimizing a rhythm modeling module according to the real Tibetan rhythm tag and the predicted rhythm tag to obtain a target rhythm encoder. The invention also provides a front-end modeling system and equipment for voice synthesis of the conoba Tibetan language, a storage medium and a program product.

The invention provides an automatic voice work order generation system and method based on multi-modal processing, and relates to the technical field of voice work order generation, and the system comprises a multi-modal input layer which is used for collecting voice data, equipment metadata and auxiliary modal data; the voice processing layer is used for carrying out audio preprocessing on the voice data to extract audio feature information and translating the audio feature information into text information; the semantic analysis layer is used for receiving equipment metadata, auxiliary modal data, audio feature information and transliteration text information, and performing multi-modal field adaptive analysis in combination with historical work order data; and the work order output layer is used for receiving the structural work order elements and then converting the structural work order elements into standard work orders. Through the method and the device, the technical problem that the work order generation efficiency is further influenced due to low voice recognition accuracy caused by high manual intervention dependence and multi-modal data processing splitting in the prior art can be solved, and the work order generation efficiency is improved by realizing end-to-end automatic conversion from the voice data to the structured work order.

The embodiment of the invention discloses a conference information processing system and a terminal, and belongs to the technical field of computers. The system comprises a terminal, a server and an edge device, the terminal is used for carrying out voice-to-word processing on the audio data of the conference to obtain a transliteration text corresponding to the audio data; the terminal is also used for determining target equipment from the server and the edge equipment based on the confidentiality level of the conference; sending the transliteration text to the target device; the server is used for processing the transferred text under the condition of receiving the transferred text to obtain a conference summary of the conference; sending the conference summary to the terminal, and displaying the conference summary by the terminal; the edge device is used for processing the transferred text under the condition of receiving the transferred text to obtain a conference summary of the conference; and sending the conference summary to the terminal, and displaying the conference summary by the terminal. According to the system, the conference information processing process is intelligently and efficiently realized through cooperation of the multi-terminal equipment, and manual arrangement of a user on the transferred text is avoided.

Fuzzy string comparison between a first textual string of a first orthography and a second textual string of a second orthography is provided. The first and second textural strings are transliterated to a first phonetic spelling and a second phonetic spelling, respectively. Application of constraints of the first orthography on the first phonetic spelling and of constraints of the second orthography on the second phonetic spelling is detected. The detected constraints of the first orthography on the first phonetic spelling are reversed to generate a first revised phonetic spelling, and the detected constraints of the second orthography on the second phonetic spelling are reversed to generate a second revised phonetic spelling. The first revised phonetic spelling and the second revised phonetic spelling are compared to generate a similarity score for the first textual string and the second textual string.

The present application relates to the field of place name translation technology and discloses a place name translation method that combines multilingual syllable segmentation with adaptive learning. The method performs word segmentation and common name translation annotation on the source language place name address based on a general dictionary, converts the unannotated proper noun portion into an IPA phoneme sequence, and then performs syllable segmentation and translation matching on the IPA phoneme sequence of each word segment. Multiple possible candidate transliteration results are matched for each word segment. The candidate transliteration results of the proper noun segment are then combined with the common name translation result to construct a candidate set of place name and address target language translation results. The candidate set is then adaptively learned and aggregated using a deep learning algorithm to comprehensively consider the semantic information and contextual coherence of multiple transliteration versions to generate the final place name and address target language translation. The present application can solve the problems of inaccurate, stiff, and ambiguous transliterations in traditional place name and address translation methods, thereby improving translation efficiency and quality.

The invention discloses a voice transcription processing method and device, equipment and a program product. The method comprises the steps of obtaining audio data; and performing voice transcription processing on the audio data to obtain candidate transcription texts of at least two versions. Comparing the at least two versions of candidate transcription texts, identifying suspicious text segments with transcription ambiguity in the at least two versions of candidate transcription texts, and determining a text position corresponding to each suspicious text segment as a position to be filled. And for each position to be filled with the blank, extracting corresponding different text contents from all the candidate transcription texts to serve as candidate lexical items of the position to be filled with the blank. And calling a language model, and selecting matched candidate lexical items for each to-be-filled position based on context semantics of all the candidate transliteration texts to perform text completion so as to obtain a target transliteration text corresponding to the audio data.

The present disclosure provides a non-transitory computer readable medium having instructions stored thereon that, when executed by a processing device, cause the processing device to carry out an operation comprising: ingesting metadata associated with a first song including a plurality of first song lyrics; dividing the plurality of first song lyrics into corresponding lyric blocks; encoding, via a phonetic color-number pairing, each lyric block by determining a first consonantal sound of each lyric, associating the first consonantal sound with a predetermined color-number pairing, and color-coding a plurality of corresponding lyric blocks based on the predetermined color-number pairing; generating structured grids comprised of the corresponding color-coded lyric blocks and displaying the structured grids via client devices.

The invention discloses a multi-level judgment conference speaker conversion point recognition method based on text and voice representation fusion, and relates to the technical field of voice signal processing and artificial intelligence. The method comprises the following steps: firstly, preprocessing an original audio stream to extract an effective voice segment, then obtaining a transliteration text and a character timestamp through low-delay stream type voice recognition, generating a word-level timestamp, and segmenting a word-level audio segment; converting points in the fragments are identified, cross-fragment multi-dimensional features are extracted, and affiliation of adjacent fragments is discriminated through fusion of a depth discrimination model; according to the method, text and voice representation are combined, the problems of inaccurate boundary segmentation, voice mixing, sensitivity to acoustic characteristic fluctuation and the like in the prior art are effectively solved through a multi-level judgment mechanism, the accuracy and robustness of speaker conversion point recognition are improved while low delay is guaranteed, and support is provided for high-quality conference record generation.

The invention discloses a machine translation method and a related device for a class and graphic geographical name, and relates to the field of language translations. The method comprises the following steps: carrying out letter combination division on a class and graphic geographical name phrase to be translated by adopting a forward maximum matching algorithm and a reverse maximum matching algorithm; respectively calculating the mutual information value of the obtained corresponding forward letter combination division result and the mutual information value of the obtained corresponding reverse letter combination division result; and according to a comparison result of the mutual information values, determining an intermediate division result, and performing transliteration on the intermediate division result by using the class and graph Chinese translation table to obtain a transliteration result. According to the method, the to-be-translated phrases are segmented to obtain the intermediate division result which can directly perform accurate proper-name transliteration according to a class and graph Chinese translation table and has high association degree between adjacent phrases, so that the proper-name transliteration problem in class and graph geographical name translation is solved, a machine translation model can independently perform class and graph geographical name translation, and the translation efficiency is improved. The manpower consumption during place name generation is reduced, and the machine translation efficiency is improved.

The invention discloses a speech recognition method, device and system. Receiving a voice slice sent by the CCU through the websocket connection, and caching the voice slice; in response to the arrival time of a preset duration period, splicing all the cached voice slices into a complete long voice according to a caching sequence; sending the long voice to an online voice recognition server, enabling the online voice recognition server to perform voice recognition on the long voice to obtain a transcription text, and returning the transcription text; receiving a transliteration text returned by the online voice recognition server, sending the transliteration text to a large language model server, enabling the large language model server to judge the transliteration text through a preset large language model, obtaining a corresponding judgment result, and returning the judgment result; and in response to determining that the long voice is abnormal voice according to the judgment result, stopping voice slice splicing, and returning the judgment result to the CCU, so that the CCU performs processing according to a preset abnormality processing strategy. According to the method, voice recognition can be accurately realized in real time.

The invention relates to a voice recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a transliteration text which is obtained through the transliteration of a to-be-processed voice based on a language prediction label; calling a large language model to perform semantic judgment on the transcription text to obtain a first judgment result; and outputting a transcription result of the to-be-processed voice based on a confirmation result of the first judgment result on the language prediction tag. According to the embodiment of the invention, the reliability and stability of speech recognition can be improved.

Script Transliteration Input Pad

The present application relates to the technical field of place name and address translation, and discloses a place name and address translation method based on intelligent splitting and matching of a proper name library and a common name library. The method first constructs a common name library containing standard translated names and a proper name library containing transliterated proper names. After multiple rounds of segmentation of the place name and address to be translated, the word segments in each segmentation scheme are type-labeled through dual-library matching, and the optimal segmentation scheme is determined based on the labeled character coverage. Contextual semantic association analysis is further performed on the unlabeled word segments in the optimal segmentation scheme. After inferring the attribute type of the word segment, a large language model is called to generate an adapted translation. Finally, a rule engine is used to structurally reorganize the dual-library labeled translations and the inferred translations to form a standardized standard address translation. Through multiple rounds of word segmentation and labeling and optimal segmentation scheme screening, the present application can effectively solve the segmentation ambiguity problem, accurately identify the core components of the address, and thus improve the translation quality.

A method for converting speech in one of a plurality of input languages into text using machine transliteration and transfer learning is disclosed. The method includes a training stage. The training stage includes receiving a training set of a plurality of audio files and an input text corresponding to the audio input in any input language using the speech recognition engine; transliterating the training set to transform the input text into transliterated text that includes characters of a base language and training acoustic model with the plurality of audio files and corresponding transliterated text using transfer learning. The method further includes an inference stage. The inference stage includes performing decoding on output of the trained acoustic model to generate text includes characters of the base language at inference and transliterating the generated text to output text includes characters in input language using reverse transliteration.

The present application relates to the field of intelligent translation technology, and specifically discloses a method for intelligent place name and address translation based on multilingual syllable segmentation, which introduces a deep learning algorithm to make adaptive translation strategy decisions for each vocabulary unit in the source language place name and address text, so as to automatically distinguish between proper nouns that should be transliterated and common vocabulary that should be translated using a dictionary, and performs fine-grained syllable-level segmentation and target language syllable mapping on proper nouns marked as transliterated to achieve accurate transliteration of proper nouns; at the same time, for common vocabulary marked as dictionary translation, a dictionary matching algorithm is used for accurate translation, and then the transliteration results of proper nouns and the translation results of common vocabulary are standardized and reorganized to obtain the place name and address translation results. The present application can effectively improve the translation effect of proper place names when mixed with common words, and enhance the translation accuracy and flexibility of place names and addresses in a multilingual environment.

The embodiments of the present application belong to the field of R&D design and audio processing technology, and are applied to audio generation scenarios. They relate to an audio generation method, apparatus, device, and storage medium thereof, which obtain target text data for audio generation; input the target text data into a pre-trained audio generator; identify the scale selection instruction obtained through human-computer interaction; and obtain the expected audio data output by the audio generator based on the scale selection instruction. The audio generation method described in this application is applied to multi-scale audio generation scenarios, especially in multi-language broadcasting or intelligent voice customer service follow-up scenarios. It can generate more detailed and high-quality multi-language transliterations based on the differences in audio language, audio time, and audio frequency range, which is more automated and intelligent, and provides more international broadcasting or inquiry services for customers of different languages.

An example electronic device may include a memory configured to store instructions and a processor electrically connected to the memory and configured to execute the instructions. When the instructions are executed by the processor, the processor may be configured to create an automatic speech recognition (ASR) language model including information about a plurality of candidate transliterations for a variously utterable text, based on a context of a user indicating a situation of the user, a basic language model, or a customized language model and update the customized language model in response to an utterance of the user matching one of the plurality of candidate transliterations.

An electronic device includes a processor and a memory to store a dictionary and index information. The memory can store instructions to cause the processor to: extract a first text in a first language included in data; determine whether or not a transliteration pair is included in the dictionary; if the transliteration pair is identified, index the first text and at least one second text to the data and store same in the index information; and, if the transliteration pair is not identified, extract at least one third text in the second language by inputting the extracted first text to a machine learning model for transliterating an input in the first language into the second language and extracting same, and index the first text and the at least one third text which has been extracted to the data and store same in the index information.

The disclosure provides a transliteration model training method and device, equipment and a storage medium, relating to the fields of deep learning, natural language processing and the like artificial intelligence, and the implementation scheme is as follows: a plurality of sample texts to be translated of a first language type are obtained; a plurality of labeled sample texts corresponding to the plurality of sample texts to be translated are obtained, the first language type and a second language type are different, and the second language type is a language type corresponding to the labeled sample texts; a plurality of labeled pronunciation information between the plurality of sample texts to be translated and the corresponding plurality of labeled sample texts is determined; and an initial transliteration model is trained according to the plurality of sample texts to be translated, the plurality of labeled sample texts, and the plurality of labeled pronunciation information to obtain a target transliteration model. Thus, the feature of the pronunciation dimension between the sample texts to be translated and the labeled sample texts is effectively modeled, the influence of pronunciation ambiguity on the translation effect is avoided, and the translation accuracy and effect are improved.

A system and a method for converting text in one of a plurality of input languages into a text in a second language using phonetic based transliteration are disclosed. The method includes receiving (802) an input text in a first script from a user; phonetically mapping (804) each character of the input text with a second script corresponding to the second language; validating (806) permutations of mapping of each input character with each character of second script and transliterating (808) input text in first script into an output text in second script. A transliteration engine (106) is configured to transliterate input text of first language into the output text of second language. The transliteration engine (106) includes a data reception module (108), a data transformation module (110), a training module (112), an inference module (114), and a database (116).

Systems and methods of computational transliteration of an input sequence of characters in a source language such as Thai to a target language such as Latin. The output may include Romanization of Thai names. The output sequence may be used for machine transliteration understanding of words such as proper nouns. A system may execute a higher-order function that calls a reducing function that iterates through sliding windows of an input sequence and updates, with each iteration, an accumulator map that includes a vector of characters and an indication of a number of characters that can be skipped when processing the next window. Each window includes multiple characters in the input sequence for context-based transliteration using contextual transcription rules. Characters can be skipped when they have already been processed in a previous sliding window. Transliteration of certain source languages may include transposition, deletion, insertion, and transcription.

The invention provides a voice recognition illusion suppression method and system based on a double-decoding decision network, and a medium. The method comprises the steps of obtaining to-be-recognized voice data; decoding the voice data by adopting a biased decoding model to obtain a customized transliteration result A; decoding the voice data by adopting an unbiased decoding model to obtain a preliminary transcription result B; inputting representation tensors of the customized transliteration result A and the preliminary transliteration result B into a pre-training semantic encoder to obtain advanced representation, and inputting the advanced representation into a trained predictive projection network to obtain illusion probability; comparing the illusion probability with a set discrimination threshold value, and if the illusion probability is smaller than or equal to the discrimination threshold value, outputting a preliminary transcription result B; if so, outputting a customized transfer result A; according to the method, two paths of decoding are performed on the same section of voice in parallel, so that illusion insertion errors caused by prompt words can be effectively eliminated, and the overall recognition accuracy is obviously improved in a scene containing user-customized hot words.

The present application relates to the field of place name and address translation technology, and specifically discloses a place name and address translation tool and method that integrates artificial intelligence proper noun and common noun splitting. The tool adopts deep learning-based artificial intelligence technology to perform semantic analysis and contextual semantic association enhancement on the place name and address to be translated, and facilitates the proper noun and common noun splitting and type annotation of the place name and address to be translated based on the Prompt-driven LLM model. Based on a preset geographic terminology knowledge base, an iterative minimum translation unit determination is performed on each place name and address segment, and a recursive decomposition is performed on non-minimum unit segments until all segments meet the minimum translation granularity requirement. Each translation unit is subjected to transliteration or standard translation conversion, and a standard place name and address translation is output through normalization processing. This can effectively solve the problems of inaccurate semantic splitting and insufficient standardization of translated names in traditional translation, and improve the quality of place name and address translation in scenarios such as cross-border map services and international logistics.