METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION
A system for optimizing the selection of transcription engines is provided. The system includes an alphanumeric preprocessor, an audio analysis preprocessor, a categorical preprocessor, and a continuous variable preprocessor which is configured to receive outputs from the alphanumeric, the audio analysis, and the categorical preprocessors and to generate data used by a modeling module to create a list of ranked transcription engines. The transcription engines are used to generate transcriptions of media data sets.
1 . A system for optimizing selection of transcription engines using a combination of preprocessors, comprising:
a database including one or more media data sets;
an alphanumeric preprocessor configured to convert one or more features, of a selected media data set of the one or more media data sets, having alphanumeric values to real and integer values;
an audio analysis preprocessor configured to generate mel-frequency cepstral coefficients from the selected media data set;
a categorical preprocessor configured to categorize the one or more features of the selected media data set, wherein the categorization comprises a frequency value; and
a continuous variable preprocessor configured to ingest outputs from the alphanumeric, the audio analysis, and the categorical preprocessors and to generate a first output data;
one or more machine learning models configured to ingest the first output data and to generate a ranked list of transcription engines;
a transcription engine, selected from the ranked list of transcription engines, configured to ingest the first output data and to generate a transcript for the selected media data set.
2 . The system of claim 1 , wherein the one or more features include at least a file type and an encoding format.
3 . The system of claim 1 , wherein the database is a temporal elastic database.
4 . The system of claim 1 , wherein the continuous variable preprocessor winsorizes and standardizes the ingested outputs from the alphanumeric, the audio analysis, and the categorical preprocessors.
5 . The system of claim 1 , wherein the media data sets include time weighted data.
6 . The system of claim 1 , wherein the transcription engine selected from the ranked list of transcription engines is the top ranked engine.
7 . The system of claim 6 , wherein the top ranked engine has a proper permission.
8 . The system of claim 1 , wherein the list of transcription engines is ranked based on predicted accuracy.
9 . The system of claim 1 , wherein the alphanumeric preprocessor, the audio analysis preprocessor, and the categorical preprocessor are executed substantially in parallel.
10 . The system of claim 1 , wherein the transcript includes a plurality of searchable multi-dimensional arrays of transcribed words or silent periods, wherein each transcribed word or silent period is associated with a confidence score.
11 . A computer-implemented method for optimizing the selection of transcription engines using a combination of selected, ordered preprocessors, comprising:
one or more network-connected servers, each including a processor and non-transitory computer readable memory storing instructions that, when executed by the processor:
convert, by an alphanumeric preprocessor, one or more features, of a selected media data set, having alphanumeric values to real and integer values;
generate, by an audio analysis preprocessor, mel-frequency cepstral coefficients from the selected media data set;
categorize, by a categorical preprocessor, the one or more features of the selected media data set, wherein the classification includes a frequency value;
generate, by a continuous variable preprocessor, a first output data using outputs from the alphanumeric, the audio analysis, and the categorical preprocessors;
generate, by one or more machine learning models, a ranked list of transcription engines using the first output; and
generate, by a transcription engine selected from the ranked list of transcription engines, using the first output data, a transcript for the selected media data set.
12 . The method of claim 11 , wherein the one or more features include at least a file type and an encoding format.
13 . The method of claim 11 , wherein the number of generated mel-frequency cepstral coefficients is between ten and twenty.
14 . The method of claim 11 , wherein the continuous variable preprocessor winsorizes and standardizes the outputs from the alphanumeric, the audio analysis, and the categorical preprocessors.
15 . The method of claim 11 , wherein the media data sets include time-weighted data.
16 . The method of claim 11 , wherein the transcription engine selected from the ranked list of transcription engines is the top ranked engine.
17 . The method of claim 11 , wherein the top ranked engine has a proper permission.
18 . The method of claim 11 , wherein the list of transcription engines is ranked based on predicted accuracy.
19 . The method of claim 11 , wherein the alphanumeric preprocessor, the audio analysis preprocessor, and the categorical preprocessor are executed substantially in parallel.
20 . The method of claim 11 , wherein the transcript includes a plurality of searchable multi-dimensional arrays of transcribed words or silent periods, wherein each transcribed word or silent period is associated with a confidence score.