You can use a speech recognition software to recognize the beginning of your recording or a speech recognition API to code this.
If You want to implement this yourself, you can use a VAD algorithm to isolate the note at the beginning of the file, and then apply by a speech recognition method for classifying into a category.
With respect to your edit, and as mentioned in my original answer, you can use an 'off the shelf' speech recognition api. The link I gave you is for python but many exist in all platforms (including MATLAB). You will still need to use some VAD or time convention (the first second) for the stamp section.
If you are more interested in getting the job done, you can use a program that performs speech recognition and manually cut the note at the beginning.
My original answer assumed you want to implement this yourself, as this is a signal processing stack exchange and not a 'get the job done' stack exchange. I will, therefore, leave it for the benefit of anyone stumbling this question who does want to implement such a task themselves.
There are a number of valid solutions for what you are describing. I am not sure how familiar are you with deep learning (DL) methods but to my best knowledge, you are better off using them for classification methods.
Enunciation won't help you at all, as audio is usually a non-stationary statistical process.
You may avoid the note all together (as you mentioned) and proceed to scene recognition. The Idea, here, is to use a deep network for classification of the scenery with respect to sound. This is a code that tries to cope with this task, and this is a review of methods.
On the other hand, you may use speech recognition, either speaker-dependent or speaker-independent. The benefits here are that speech recognition is a vastly researched field with many robust solutions. In my personal opinion, it will produce a lower error rate in your task. If you are taking this approach, you can isolate the note in the beginning in two methods. Either you trim the first $x$ seconds, which you decided to dedicate to noting purposes and work on them, or you use a voice activity detection (VAD) algorithm to isolate the word, such as this code. The speech recognition can be done either by a neural network or using a model-based method such as HMM.
With either of these methods, you can't avoid recording and labeling many examples of your speech or audio scene for the method to train on. This is usually a tedious work.