The Google command set has a lot of bad samples about diy-alexa HOT 3 CLOSED

That looks really interesting!

You're definitely right about the quality of the dataset, it's pretty bad, but there don't seem to be many options around.

I think you are right and creating a good clean dataset for a wake word would improve things considerably - and picking a sensible wake word as you suggest with more synonyms/phoneme would also be very sensible.

I'll have to take a look at HMG and do some testing.

Thanks for doing this research!

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Maybe we could share a spreadsheet or database that contains cleaned datasets with regional / gender metadata or datasets.

As said it is quite easy to create a word timing transcript been searching to find a word extractor to save the dev as guessing there will be one that exists somewhere that can process voice audio.
Last time I did it manually and lost the dataset on a windows reinstall.
HMG is just tensorflow but the GUI is just simple and good helped me a lot and it wasn't until I pruned the bad samples I became aware how much the 'garbage in' syndrome has effect on models, the GUI does make things easier.

Its surprising that apart from the Google command set there seems an absence of 'keyword' datasets but they can be extracted from ASR ones.

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I loaded up HMG again its still a work in progress but the majority works.
Keep the standard MFCC settings add a single hotword and all other to non hotword.
Did with 'marvin' and yeah boy a load of bad samples that really does effect overall accuracy without a prune.
Also if you want to add some of your own record some at 16kHz 16 bit before you start as if you check for duplicates it will remove missing from the dataset but adding yet doesn't seem to be implemented.

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