The following two paragraphs were written by Chuck Runquist (node 607). The subject was: When to correct the misrecognitions. I have quoted the two paragraphs here so that anyone who chooses to respond to my query wouldn't have to look it up.

I'm very new to speech recognition and I'm using Dragon preferred.

Could someone clarify for me the first sentence of the second paragraph. I'm not getting just what the conditions referred to are. Thanks.

Here are the two paragraphs:

Not exactly correct. If the playback is clearly what you said, or intended to say, and the correct word or phrase is in the selections shown, then select the correct word or phrase, but don't train it. Again, the reason is that what you said originally was said in the context of your total dictation. If you train your correction under this condition you run the risk of training the word or phrase out of the total context of you original dication (i.e., we tend to speak differently during normal speaking than when we pronounce a word or phrase during correction). This changes the Acoustic Model representation for the way you normally speak. If the difference is significant, then you alter the spoken form such that it may result in increasing the propensity for the correction to be misrecognized. It is important to note that a one time correction/training will not generally result in future misrecognitions. However, doing this continually eventually alters the Acoutic Model in a negative way, thus causing accuracy degredation, which requires correction, which further exacerbates the problem. In short, it is the repitition of continually correcting and training the same word or phrase over time that is one of the causes of accuracy degredation.

The other part of this is that if a word or phrase is misrecognized, you should always correct it, but only train it under the above conditions. DNS does not learn from your normal dictation. The speech models and the recognizer work on the principle that if you do not make a correction, what was said and what was displayed are correctly associated. Only corrections train the speech models, and DNS only learns from correcting misrecognitions. Acoustic and Language models are distinctly different and how each is modified is distinctly different. The Language Model is only modified by document analysis. The Acoustic Model is only modified by training. When performing corrections, selecting the correct word or phrase from the selection options, or typing the correction, is based on the document analysis algorithm and changes the Language Model (context). Training changes the Acoustic Model. Performing corrections links the Acoustic Model to the Language Model either in terms of what was said originally or in terms of any training done during correction. It is critically important to remember these distinctions. This is the way SR works. Unfortunately, it is not subject to debate. SR algorithms work the way they do whether we disagree with them or not.