When I am decoding my trained Acoustic model, I have noticed that in outputs
it can recognise all the words but it aso put few of its own. Firstly I
thought this issue can be solved with tuning LM weight but then after I tuned
to get my best wer. The maximum error i find is insertion of extra words. So I
assumed that if I increase/tune my word insertion penalty (wip) then this
error can decrease a little bit.
So I have tested varying -wip parameter.
Can any one please suggest what can I do to overcome this extra insertion of
words in the output.
Thank You very much for all the help
Example of o/p:
find *** flight on delta to j f k from cincinnati
find A flight on delta to j f k from cincinnati
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Can any one please suggest what can I do to overcome this extra insertion of
words in the output.
Insertion can be caused by multiple things, sometimes it's acoustic model
issue and better acoustic model can help, sometimes it's a language model
issue and you need a better language model. Sometimes it's graph construction
issue and wip can help. Error analysis is a complex analysis of the whole
training and testing process.
You need to provide whole dataset you are using in order to get an answer on
your question.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hello,
When I am decoding my trained Acoustic model, I have noticed that in outputs
it can recognise all the words but it aso put few of its own. Firstly I
thought this issue can be solved with tuning LM weight but then after I tuned
to get my best wer. The maximum error i find is insertion of extra words. So I
assumed that if I increase/tune my word insertion penalty (wip) then this
error can decrease a little bit.
So I have tested varying -wip parameter.
Can any one please suggest what can I do to overcome this extra insertion of
words in the output.
Thank You very much for all the help
Example of o/p:
find *** flight on delta to j f k from cincinnati
find A flight on delta to j f k from cincinnati
Insertion can be caused by multiple things, sometimes it's acoustic model
issue and better acoustic model can help, sometimes it's a language model
issue and you need a better language model. Sometimes it's graph construction
issue and wip can help. Error analysis is a complex analysis of the whole
training and testing process.
You need to provide whole dataset you are using in order to get an answer on
your question.