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Comparing strings for mutability
What is the difference between String and string in C#?How do I iterate over the words of a string?How do I read / convert an InputStream into a String in Java?Case insensitive 'Contains(string)'How do I make the first letter of a string uppercase in JavaScript?How to replace all occurrences of a string in JavaScriptHow to check whether a string contains a substring in JavaScript?Does Python have a string 'contains' substring method?How do I convert a String to an int in Java?Why is char[] preferred over String for passwords?
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I am using a speech transcription engine and it returns results with the associated confidence score that a given result is correct. I would like to look for patterns that show similarities between alternatives (implying a high likelihood that substring was correctly transcribed) and determine which letters/indexes are different amongst alternatives (implying a lower likelihood that they had been correctly transcribed).
For example "price" is the correct transcription and the sequence "ric" would be tagged as likely to be correct:
price,0.8999
perica,0.6221
price,0.7847
perice,0.7208
pricee,0.7843
parice,0.6866
parica,0.5879
pricea,0.7861
brice,0.6957
prize,0.7438
pric,0.8811
terica,0.4677
brica,0.5805
pericee,0.6420
priceee,0.6976
prace,0.7502
trica,0.5805
terice,0.5664
trice,0.6957
prico,0.7828
Also, the alternatives need to be weighted against their confidence score so as to avoid outliers (low confidence score alternatives) from having as significant an impact as high confidence score alternatives.
The point of this is to be able to keep track of letter combinations that the transcription engine struggles with more than others.
Does anyone know of any existing algorithms that could be adapted to such a task?
Thanks
string algorithm comparison
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
add a comment |
I am using a speech transcription engine and it returns results with the associated confidence score that a given result is correct. I would like to look for patterns that show similarities between alternatives (implying a high likelihood that substring was correctly transcribed) and determine which letters/indexes are different amongst alternatives (implying a lower likelihood that they had been correctly transcribed).
For example "price" is the correct transcription and the sequence "ric" would be tagged as likely to be correct:
price,0.8999
perica,0.6221
price,0.7847
perice,0.7208
pricee,0.7843
parice,0.6866
parica,0.5879
pricea,0.7861
brice,0.6957
prize,0.7438
pric,0.8811
terica,0.4677
brica,0.5805
pericee,0.6420
priceee,0.6976
prace,0.7502
trica,0.5805
terice,0.5664
trice,0.6957
prico,0.7828
Also, the alternatives need to be weighted against their confidence score so as to avoid outliers (low confidence score alternatives) from having as significant an impact as high confidence score alternatives.
The point of this is to be able to keep track of letter combinations that the transcription engine struggles with more than others.
Does anyone know of any existing algorithms that could be adapted to such a task?
Thanks
string algorithm comparison
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
add a comment |
I am using a speech transcription engine and it returns results with the associated confidence score that a given result is correct. I would like to look for patterns that show similarities between alternatives (implying a high likelihood that substring was correctly transcribed) and determine which letters/indexes are different amongst alternatives (implying a lower likelihood that they had been correctly transcribed).
For example "price" is the correct transcription and the sequence "ric" would be tagged as likely to be correct:
price,0.8999
perica,0.6221
price,0.7847
perice,0.7208
pricee,0.7843
parice,0.6866
parica,0.5879
pricea,0.7861
brice,0.6957
prize,0.7438
pric,0.8811
terica,0.4677
brica,0.5805
pericee,0.6420
priceee,0.6976
prace,0.7502
trica,0.5805
terice,0.5664
trice,0.6957
prico,0.7828
Also, the alternatives need to be weighted against their confidence score so as to avoid outliers (low confidence score alternatives) from having as significant an impact as high confidence score alternatives.
The point of this is to be able to keep track of letter combinations that the transcription engine struggles with more than others.
Does anyone know of any existing algorithms that could be adapted to such a task?
Thanks
string algorithm comparison
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
I am using a speech transcription engine and it returns results with the associated confidence score that a given result is correct. I would like to look for patterns that show similarities between alternatives (implying a high likelihood that substring was correctly transcribed) and determine which letters/indexes are different amongst alternatives (implying a lower likelihood that they had been correctly transcribed).
For example "price" is the correct transcription and the sequence "ric" would be tagged as likely to be correct:
price,0.8999
perica,0.6221
price,0.7847
perice,0.7208
pricee,0.7843
parice,0.6866
parica,0.5879
pricea,0.7861
brice,0.6957
prize,0.7438
pric,0.8811
terica,0.4677
brica,0.5805
pericee,0.6420
priceee,0.6976
prace,0.7502
trica,0.5805
terice,0.5664
trice,0.6957
prico,0.7828
Also, the alternatives need to be weighted against their confidence score so as to avoid outliers (low confidence score alternatives) from having as significant an impact as high confidence score alternatives.
The point of this is to be able to keep track of letter combinations that the transcription engine struggles with more than others.
Does anyone know of any existing algorithms that could be adapted to such a task?
Thanks
string algorithm comparison
string algorithm comparison
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
asked Mar 9 at 7:44
Harry StuartHarry Stuart
412413
412413
add a comment |
add a comment |
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