Yeah think so that it could select Raven templates might need to do a KWS failure once to switch Raven Profiles (not sure how long it needs to run for accuracy, but a single KWS failure to then switch isn’t that bad a proposition to accuracy gained, as only fails once when switch is needed)
but yeah multi-user templates could be a thing without load increase as raven would be switching profiles rather than trying to process profiles in parallel.
Has quite a few uses from maybe even voice biometrics and security.
It can also do the same for ASR that ASR can also switch profiles (models) based on diarization.
Say swap between 2 models such as gender to gain accuracy.
I like the idea of going native aka Rhasspy Rover but really that is all that is needed is Raven and RTP audio & control.
Its quite possible to have multiple simple satelites of the Simple Rover layout and use the result sensitivity to mix audio and deselect bad input from a distributed satellite array or just use best satellite single input signal, which a simple local KWS recognition can very much attain as that can be RTP info from each satellite to an ASR/Intent server that accompanies a stream.
In fact it can be just a asound channel mix as the KWS failures don’t initiate a stream.
MFCC+DTW already does speaker identification as the template is speaker specific.
That’s why I wrote above about multiple keywords (each with multiple templates) so each family member can provide multiple templates for the same keyword.
With this, Raven will be able to detect which person (keyword) has uttered the wakeword (even if it is the same for everyone).
I’ve tested with everyone in the household and it works flawlessly
Yeah but what we where talking is that your MFFC+VAD+DTW are all separate libs and multiples of load whilst they might not have to be.
If you are running against multiple keywords and multiple profiles then surely that is multiples of load also?
So if you can do diarization via VAD to select current profile at least then its only the multiples of keywords if you have them.
Also if you use the Julia Lib Vad+MFCC FFT use are off the same load and that is why the Julia Lib was forwarded so webrtcvad could be dropped from the load.
The diarization of JuliaMFCC is just another bonus that Julia lib has that could be used to cut load.
I’m quite satisfy at the moment, but now in Production with lot of current conversation around, I have a few false positive and increase sensibility step by step. would hit the point where sensitivity is too high.
Actually I use Minimum Matches 1 and VAD sensitivity 1 (average checked)
Does Minimum Matches to 2 provide good result ? Does it allow to decrease sensitivity with still good detection and low false positive ? What about cpu charge ?
Anyone sharing experience regarding these settings would help I will have to get some Pis for testing setup but right now, just got production setup, can’t break it
I will have to get some Pis for testing setup but right now, just got production setup, can’t break it 