Hi all,
I would like to ask from all the experts here, in order to get the better view on the usage of cleaned signals which already removed the echo using few types of adaptive algorithms with method of AEC.(acoustic echo cancellation)
How the significance of MSE and PSNR can improve in the classification processes? Which i mean normally we evaluate using the technique of WER, Accuracy and may EER too.Is there any kind connectivity of MSE and PSNR values in terms of improving those classification metrics.?
wish to have the clarification on this.
Thanks much
I think so since we get lower WER during keywordspotting when SNR has been improved
It is one of the old issues in a speech recognition research field. That is on the relationship between any speech enhancement technique and the classification accuracy.
As far as I know, both the MSE and PSNR are frequently used for improving the quality of the input. They are known as useful in reducing WER. However, the relationship with recognition accuracy is not directly proportional.
Enhancing a noisy signal in terms of MSE or PSNR means that you may have a good quality of the input but there is a risk. Sometimes, unexpected artifacts are produced by the speech enhancement techniques and WER can be increased in the worst case.
So, in phonemic classification task, matched condition is more crucial. And in the case of mismatched condition between train and test, MSE and PSNR are somewhat related to WER, but not directly. It is a case-by-case study.
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