An electronic apparatus which acquires input data to be input into a TTS module for outputting a voice through the TTS module, acquires a voice signal corresponding to the input data through the TTS module, detects an error in the acquired voice signal based on the input data, corrects the input data based on the detection result, and acquires a corrected voice signal corresponding to the corrected input data through the TTS module.

A method and an apparatus for generating a model are provided. The method includes: acquiring a sample set including sample sentences and labeling knowledge corresponding thereto; and selecting a sample from the sample set, and performing following training steps: inputting a sample sentence into a first initial model to generate first prediction knowledge corresponding to the sample sentence; inputting the first prediction knowledge into a second initial model to generate a first prediction sentence corresponding to the first prediction knowledge; inputting labeling knowledge into the second initial model to generate a second prediction sentence corresponding to the labeling knowledge; inputting the second prediction sentence into the first initial model to generate a second prediction knowledge corresponding to the second prediction sentence; determining a first reward signal; and training, using a reinforcement learning method based on the first reward signal to obtain a first model.

A method and an apparatus for generating a model are provided. The method includes: acquiring a sample set including sample sentences and labeling knowledge corresponding thereto; and selecting a sample from the sample set, and performing following training steps: inputting a sample sentence into a first initial model to generate first prediction knowledge corresponding to the sample sentence; inputting the first prediction knowledge into a second initial model to generate a first prediction sentence corresponding to the first prediction knowledge; inputting labeling knowledge into the second initial model to generate a second prediction sentence corresponding to the labeling knowledge; inputting the second prediction sentence into the first initial model to generate a second prediction knowledge corresponding to the second prediction sentence; determining a first reward signal; and training, using a reinforcement learning method based on the first reward signal to obtain a first model.

A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.

A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.

A computer system may be configured to: execute a first query associated with a first panel; display the first panel in a user interface based on first display settings of the first panel, the first panel displaying at least a portion of the result of the first query, the result of the first query associated with a variable; execute a second query associated with a second panel, wherein the second query refers to the variable associated with the first query; display the second panel in the user interface based on second display settings of the second panel, the second panel displaying at least a portion of the result of the second query; and in response to user input changing the displayed result in the first panel: re-execute the second query; and update the display of the second panel in the user interface based on results of the re-executed second query.

A computer system may be configured to: execute a first query associated with a first panel; display the first panel in a user interface based on first display settings of the first panel, the first panel displaying at least a portion of the result of the first query, the result of the first query associated with a variable; execute a second query associated with a second panel, wherein the second query refers to the variable associated with the first query; display the second panel in the user interface based on second display settings of the second panel, the second panel displaying at least a portion of the result of the second query; and in response to user input changing the displayed result in the first panel: re-execute the second query; and update the display of the second panel in the user interface based on results of the re-executed second query.

Systems and methods are provided for generating textual embeddings by tokenizing text data and generating vectors to be provided to a transformer system, where the textual embeddings are vector representations of semantic meanings of text that is part of the text data. The vectors may be averaged for every token of the generated textual embeddings and concatenating average output activations of two layers of the transformer system. Image embeddings may be generated with a convolutional neural network (CNN) from image data, wherein the image embeddings are vector representations of the images that are part of the image data. The textual embeddings and image embeddings may be combined to form combined embeddings to be provided to the transformer system.

Systems and methods are provided for generating textual embeddings by tokenizing text data and generating vectors to be provided to a transformer system, where the textual embeddings are vector representations of semantic meanings of text that is part of the text data. The vectors may be averaged for every token of the generated textual embeddings and concatenating average output activations of two layers of the transformer system. Image embeddings may be generated with a convolutional neural network (CNN) from image data, wherein the image embeddings are vector representations of the images that are part of the image data. The textual embeddings and image embeddings may be combined to form combined embeddings to be provided to the transformer system.

A method and electronic device are provided for processing data. Analog text included in a document is detected. A first area of the analog text that converts to digital text and a second area of the analog text that does not convert to the digital text are determined. The digital text is displayed in the first area that converts to the digital text, and an image of at least a portion of the analog text is displayed in the second area that does not convert to digital text.