Techniques for predicting punctuation and casing using multimodal fusion are described. An exemplary method includes processing generated text by: tokenizing the generated text into sub-words, and generating a sequence of lexical features for the sub-words using a pre-trained lexical encoder; processing audio of the audio by: generating a sequence of frame level acoustic embeddings using a pre-trained acoustic encoder on the audio, and generating task specific embeddings from the frame level acoustic embeddings; performing multimodal fusion of the sub-word level acoustic embeddings and the sequence of lexical features by: aligning the task specific embeddings to the sequence of lexical features, and combining the sequence of lexical features and aligned acoustic sequence; predicting punctuation and casing from the combined sequence of lexical features and aligned acoustic sequence; concatenating the sub-words of the text, and applying the predicted punctuation and casing; and outputting text having the predicted punctuation and casing.

A method for automatically detecting errors in at least one data entry in a database, the at least one data entry including an input string of characters that do not match at least one predefined string of characters. The method includes generating a first image map; generating at least one classification parameter by comparing the first image map to a second image map, the second image map based at least partially on the predefined string of characters; determining that the input string of characters correlates to the predefined string of characters; and modifying the at least one data entry to match the predefined string of characters in response to determining that the input string of characters correlates to the predefined string of characters. Various other methods and systems for automatically detecting errors in at least one data entry in a database are also disclosed.

Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.

A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.

Information is read, which relates to an array of objects for input that have been displayed on a display unit upon input of input information. Whether an input object of the input information that is displayed on the display unit has been touched is determined. When the input object is determined as having been touched, the touched input object is recognized as an object to be corrected. A correction candidate object based on the array of the objects for input is displayed in the vicinity of the object to be corrected. Whether the correction candidate object has been touched is determined. When the correction candidate object is determined as having been touched, the object to be corrected is replaced with the touched correction candidate object.

Information is read, which relates to an array of objects for input that have been displayed on a display unit upon input of input information. Whether an input object of the input information that is displayed on the display unit has been touched is determined. When the input object is determined as having been touched, the touched input object is recognized as an object to be corrected. A correction candidate object based on the array of the objects for input is displayed in the vicinity of the object to be corrected. Whether the correction candidate object has been touched is determined. When the correction candidate object is determined as having been touched, the object to be corrected is replaced with the touched correction candidate object.

A method of identifying text (e.g., a sentence or sentence portion) in a word processing text editor; automatically identifying a domain-specific deep-learning neural network that corresponds to an identified context, from among one or more domain-specific deep-learning neural networks; automatically identifying at least one suggested replacement word using the identified domain specific deep-learning neural network that corresponds to the identified context; and automatically controlling a display to display a user interface that includes functionality that presents prompt information that includes the at least one suggested replacement word. Changes for errors that are common in academic papers written by non-native speakers may be suggested.

The present disclosure describes techniques of synchronizing subtitles in live broadcast The disclosed techniques comprise obtaining a source signal and a simultaneous interpretation signal in a live broadcast; performing voice recognition on the simultaneous interpretation signal in real-time to obtain corresponding translation text; delaying the simultaneous interpretation signal to obtain a first delayed signal; delaying the source signal to obtain a second delayed signal; obtaining proofreading results of the first delayed signal and the corresponding translation text; determining proofread subtitles based on the proofreading results; and sending the proofread subtitles and the second delay signal to a live display interface.

The present disclosure describes techniques of synchronizing subtitles in live broadcast The disclosed techniques comprise obtaining a source signal and a simultaneous interpretation signal in a live broadcast; performing voice recognition on the simultaneous interpretation signal in real-time to obtain corresponding translation text; delaying the simultaneous interpretation signal to obtain a first delayed signal; delaying the source signal to obtain a second delayed signal; obtaining proofreading results of the first delayed signal and the corresponding translation text; determining proofread subtitles based on the proofreading results; and sending the proofread subtitles and the second delay signal to a live display interface.