Patent ID: 11922947
Assignee: SAS INSTITUTE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 22:
23. A computer-implemented system comprising:
one or more processors;
a memory;
a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising:
constructing a transcript correction training data corpus comprising a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes:
an incorrect audio transcription of a target piece of audio data, wherein the incorrect audio transcription is assigned an incorrect audio transcription label;
a correct audio transcription of the target piece of audio data, wherein the correct audio transcription is assigned a correct audio transcription label; and
a transcript correction identifier that, when applied to a model input comprising a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript;

configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus;
constructing an anomalous transcript correction training data corpus comprising a plurality of anomalous audio transcription training data samples, wherein each of the plurality of anomalous audio transcription training data samples includes:
an anomalous training sample pairing between (a) an anomalous audio transcript of a target audio data segment and (b) an annotated audio transcript of the target audio data segment, wherein:
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples relates to an inaccurate audio transcription representation of the target audio data segment; and
each anomalous audio transcript of the plurality of anomalous audio transcription training data samples was computed by the audio transcript correction machine learning model;

adapting the audio transcript correction machine learning model to an adapted audio transcript correction machine learning model based on a training of the audio transcript correction machine learning model using the anomalous transcript correction training data corpus;
replacing the audio transcript correction machine learning model with the adapted audio transcript correction machine learning model based on one or more model replacement efficacy metrics computed for the adapted audio transcript correction machine learning model satisfying a minimum model replacement efficacy value; and
executing the adapted audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service based on the adapted audio transcript correction machine learning model satisfying a minimum audio transcript correction efficacy value.