METHODS AND SYSTEMS FOR TRANSCRIPTION

1 . A method for transcription a media file using one or more processors, the method comprising:

generating, using a trained machine learning model, a list of transcription engines based at least on a feature of a media file;

requesting a first transcription engine, from the list of transcription engines, to transcribe the media file;

receiving, from the first transcription engine, one or more transcribed portions of the media file in response to requesting the first transcription engine to transcribe the media file;

identifying a first transcribed portion from the one or more transcribed portions that needs to be reexamined;

requesting a second transcription engine, from the list of transcription engines, to transcribe a first segment of the media file corresponding to the first transcribed portion that needs to be reexamined; and

receiving, from the second transcription engine, a second transcribed portion of the first segment in response to requesting the second transcription engine to transcribe the first segment of the media file.

2 . The method of claim 1 , wherein identifying the transcribed portion that needs to be reexamined comprises:

receiving transcription metadata for the first transcribed portion from the first transcription engine;

determining a confidence of accuracy for the first transcribed portions using the transcription metadata of the first transcribed portion; and

identifying the first transcribed portion as a portion that needs to be reexamined based at least on the confidence of accuracy.

3 . The method of claim 2 , wherein determining the confidence of accuracy for the first transcribed portion comprises normalizing a confidence indicator that is included in the transcription metadata.

4 . The method of claim 1 , wherein identifying the transcribed portion that needs to be reexamined comprises:

performing a textual analysis on the first transcribed portion; and

identifying the first transcribed portion as a portion that needs to be reexamined based on at least on results from the textual analysis.

5 . The method of claim 3 , wherein performing the textual analysis comprises performing at least one of:

a contextual analysis;

a grammatical analysis;

a lexical analysis;

a topical analysis;

a word composition analysis; or

a sentiment analysis.

6 . The method of claim 1 , wherein generating the list of transcription engines further comprises:

extracting audio features from the media file; and

using the trained machine learning model, generating the list of transcription engines based at least on the extracted audio features of the media file.

7 . The method of claim 1 , wherein requesting the second transcription engine comprises selecting the second transcription engine based at least on a predicted topic of the first transcribed portion.

8 . The method of claim 7 , wherein the predicted topic is determined by determining context the first transcribed portion.

9 . (canceled)

10 . The method of claim 1 , wherein requesting the second transcription engine comprises creating a specialized transcription engine to transcribe the first segment, wherein the specialized transcription engine is configured to transcribe the first segment based at least on an analysis selected from a group consisting of a contextual analysis, a grammatical analysis, a lexical analysis, a topical analysis, a cluster analysis, a word composition analysis, and a sentiment analysis.

11 . The method of claim 1 , further comprising:

determining a confidence of accuracy for the second transcribed portions based at least on transcription metadata of the second transcribed portion;

selecting a third transcription engine to transcribe the first segment when the confidence of accuracy is below the predetermined threshold; and

generating a combined transcript of the media file using transcripts received from the first and third transcription engines, wherein the first, second, and third transcription engines are different.

12 . A system for transcription a media file, the system comprising:

a memory;

one or more processors coupled to the memory, the one or more processor configured to: generate, using a machine learning model, a list of transcription engines based on the media file;

request a first transcription engine to transcribe the media file;

receive, from the first transcription engine, a plurality of transcribed portions of the media file in response to requesting the first transcription engine to transcribe the media file;

identify a first transcribed portion from the one or more transcribed portions that needs to be reexamined;

request a second transcription engine to transcribe a first segment of the media file corresponding to the first transcribed portion that needs to be reexamined; and

receive, from the second transcription engine, a second transcribed portion of the first segment in response to requesting the second transcription engine to transcribe the first segment of the media file.

13 . The method of claim 12 , wherein the one or more processors are further configured to:

receive transcription metadata for the first transcribed portion from the first transcription engine;

determine a confidence of accuracy for the first transcribed portions using the transcription metadata of the first transcribed portion; and

identify the first transcribed portion as a portion that needs to be reexamined based at least on the confidence of accuracy.

14 . (canceled)

15 . The method of claim 12 , wherein the one or more processors are further configured to:

perform a textual analysis on the first transcribed portion; and

identify the first transcribed portion as a portion that needs to be reexamined based on at least on results from the textual analysis.

16 . The method of claim 15 , wherein the textual analysis includes at least one of: a contextual analysis;

a grammatical analysis;

a lexical analysis;

a topical analysis;

a word composition analysis; and

a sentiment analysis.

17 . The method of claim 12 , wherein the one or more processors are configured to: extract audio features from the media file; and

generate, using the machine learning model, the list of transcription engines based at least on the extracted audio features of the media file.

18 . The method of claim 12 , wherein the second transcription engine is requested to transcribe the first segment based at least on a predicted topic of the first transcribed portion.

19 . The method of claim 12 , wherein the one or more processors are configured to:

create a specialized transcription engine to transcribe the first segment, wherein the specialized transcription engine is configured to transcribe the first segment based at least on an analysis selected from a group consisting of a contextual analysis, a grammatical analysis, a lexical analysis, a topical analysis, a cluster analysis, a word composition analysis, and a sentiment analysis.

20 . The method of claim 12 , further comprising:

determine a confidence of accuracy for the second transcribed portions based at least on transcription metadata of the second transcribed portion;

select a third transcription engine to transcribe the first segment when the confidence of accuracy is below the predetermined threshold; and

generate a combined transcript of the media file using transcripts received from the first and third transcription engines, wherein the first, second, and third transcription engines are different.

21 . A method for transcription a media file using one or more processors, the method comprising:

selecting, using a machine learning model, a first transcription engine to transcribe the media file;

receiving, from the first transcription engine, one or more transcribed portions of the media file in response to selecting the first transcription engine;

identifying a first transcribed portion from the one or more transcribed portions having a confidence of accuracy below a predetermined accuracy threshold;

selecting, using the machine learning model, a second transcription engine to transcribe a first segment of the media file corresponding to the first transcribed portion, the first and second transcription engines are different;

receiving, from the second transcription engine, a second transcribed portion of the first segment; and

selecting the first or second transcribed portion as transcript for the first segment of the media file based at least on confidences of accuracy of the first and second transcribed portions.

22 . The method of claim 21 , further comprising:

receiving transcription metadata for the first and second transcribed portions from respective first and second transcription engines; and

determining the confidence of accuracy for the first and second transcribed portions based at least on the transcription metadata for the first and second transcribed portions, respectively.

23 . The method of claim 22 , further comprising:

selecting, using the machine learning model, a third transcription engine to transcribe the first segment if the confidence indicator is below the predetermined threshold; and

generating a combined transcript of the media file using transcripts received from the first and third transcription engines.

24 . (canceled)

25 . (canceled)