System and method for generating disambiguated terms in automatically generated transcriptions including instructions within a knowledge domain and employing the system are disclosed. Exemplary implementations may: obtain a set of transcripts representing various speech from users; obtain indications of correlated correct and incorrect transcriptions of spoken terms within the knowledge domain; obtain a vector generation model that generates vectors for individual instances of the transcribed terms in the set of transcripts that are part of the lexicography of the knowledge domain; use the vector generation model to generate the vectors such that a first set of vectors and a second set of vectors are generated that represent the instances of the first correctly transcribed term and the first incorrectly transcribed term, respectively; and train the vector generation model to reduce spatial separation of vectors generated for instances of correlated correct and incorrect transcriptions of spoken terms within the knowledge domain.

An image of a user interface of a device is captured. A graphical object is identified in the image of the user interface. For example, a menu item or a text object is identified in the image of the user interface. An Artificial Intelligence (AI) process is used to determine a confidence score for the graphical object; the confidence score identifies a confidence of how likely a type of the graphical object can be identified. In response to the first confidence score not meeting a threshold, a zoomed in image of the graphical object is taken or a zoomed-out image of the graphical object is taken. The zoomed in image or the zoomed-out image is used to increase the confidence score to better identify the type of the graphical object.

An image of a user interface of a device is captured. A graphical object is identified in the image of the user interface. For example, a menu item or a text object is identified in the image of the user interface. An Artificial Intelligence (AI) process is used to determine a confidence score for the graphical object; the confidence score identifies a confidence of how likely a type of the graphical object can be identified. In response to the first confidence score not meeting a threshold, a zoomed in image of the graphical object is taken or a zoomed-out image of the graphical object is taken. The zoomed in image or the zoomed-out image is used to increase the confidence score to better identify the type of the graphical object.

A system described herein may provide a technique for the compression of query terms and search data against which the query terms may be evaluated. The compression may be dynamic, in that a quantity of bits used to compress the search data and query terms may be based on a quantity of unique characters included in a given query term. The compression may further include reducing the volume of search data by compressing entire words, that do not include any of the unique characters of the query term, to one particular code.

A system described herein may provide a technique for the compression of query terms and search data against which the query terms may be evaluated. The compression may be dynamic, in that a quantity of bits used to compress the search data and query terms may be based on a quantity of unique characters included in a given query term. The compression may further include reducing the volume of search data by compressing entire words, that do not include any of the unique characters of the query term, to one particular code.

Disclosed are a computer implemented method, system and platform for improving the readability and/or coherency of a conversation transcript, which include the applying of a speech disfluency detection model to identify speech disfluencies in a text transcript and to provide a corrected and/or annotated version of the conversation transcript indicating the edits made vis-à-vis the inputted text transcript.

Various embodiments discussed herein are directed to improving existing technologies by causing certain characters to be replaced at a document if such characters are likely to be an error. For example, documents generated using speech-to-text technology or Optical Character Recognition (OCR) technology often contain character errors. A scoring threshold may be utilized to determine one or more characters are not being correctly represented in the document. Alternatively or additionally, various embodiments recommend multiple character sequences as candidates to replace other characters and a user may select which of the candidates will be used for replacement.

A computer-implemented method includes: receiving, by a computing device, an input file defining correct spellings of one or more transliterated words; generating, by the computing device, suffix outputs based on the one or more transliterated words; generating, by the computing device, a dictionary that maps the suffix outputs to the one or more transliterated words; recognizing, by the computing device, an alternatively spelled transliterated word included in a document as one of the one or more correctly spelled transliterated words using the dictionary; and outputting, by the computing device, information corresponding to the recognized transliterated word.

A computer-implemented method includes: receiving, by a computing device, an input file defining correct spellings of one or more transliterated words; generating, by the computing device, suffix outputs based on the one or more transliterated words; generating, by the computing device, a dictionary that maps the suffix outputs to the one or more transliterated words; recognizing, by the computing device, an alternatively spelled transliterated word included in a document as one of the one or more correctly spelled transliterated words using the dictionary; and outputting, by the computing device, information corresponding to the recognized transliterated word.

Systems and processes for operating an intelligent automated assistant are provided. In one example process a set of words including a grammatical error is received. The process can generate, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words and further determine, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words. The process can also determine, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct and provide an indication of whether the transformed set of words is grammatically correct to the neural network.