Suki Ai, Inc.

Ahmad Badary

System and method for generating disambiguated terms in automatically generated transcripts 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 transcripts of spoken terms; use a vector generation model to generate vectors for individual instances of the correctly transcribed terms and individual instances the incorrectly transcribed terms based on text and contexts of the individual transcribed terms; and train the vector generation model to reduce spatial separation of the vectors generated for the spoken terms in the correlated correct transcripts and the incorrect transcripts.

Systems and methods for generating disambiguated terms in automatically generated transcriptions including instructions within a particular knowledge domain

Systems and methods to group terms based on context to facilitate determining intent of a command are disclosed. Exemplary implementations to train a model: obtain a set of writings within a particular knowledge domain; obtain a vector generation model that generates vectors for individual instances of the terms in the set of writings; generate a first set of vectors that represent the instances of a first term and other vectors that represent instances of the other terms of the set of writings; train the vector generation model to group the vectors of a similar context in a space of a vector space; obtain a transcript include a new term generated from user audio dictation; generate a new vector that represent the instance of the new term; obtain the space; compare the new vector with the space; utilize the new term as the first term.

Systems and methods to facilitate intent determination of a command by grouping terms based on context

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 related to the knowledge domain representing various speech from users; obtain indications of correlated correct and incorrect transcripts of spoken terms within the knowledge domain; use a vector generation model to generate vectors for individual instances of the transcribed terms in the set of transcripts that are part of the lexicography of the knowledge domain such that a first set of vectors and a second set of vectors are generated that numerically represent the instances of the first correctly transcribed term and the first incorrectly transcribed term, respectively, and in different contexts; train the vector generation model to reduce spatial separation of vectors generated for instances of correlated correct and incorrect transcripts of spoken terms within the knowledge domain.

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.

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Calgary, Canada

Ahmad Badary