> the sampling, the jargon, the 10 codes, the background noise, static on analog systems/drop outs on digital systems, rate of speech, etc

Is the reduced bandwidth really the most significant problem? Naively I'd think everything else you mentioned would matter a lot more, I'm curious how much you experimented with that specifically.

When it all comes together it's kind of a nightmare for an ASR model. I'm not sure which contributes most but I know from my prior experiences with ASR for telephony even clean speech on pristine connections does much worse with models trained on 16 kHz being fed native 8 kHz audio that gets resampled. There were plenty of times in reviewing the recordings and ASR output where I'd listen to the audio and have no idea what they said.

I've done some early work with Whisper in the telephony domain (transcribing voicemails on Asterisk and Freeswitch) and the accuracy already seems to be quite a bit worse.

Could one train an interpolation layer (eg take a bunch of 16k audio, down sample to 8k, train 8k->16k upsampler)? Or better yet (but more expensive), take whisper, freeze it, and train the upsampler on whisper’s loss.

"I understand some of those words."

Hah, in all seriousness I'm more of a practitioner in this space. If this was something I absolutely needed to get done who knows where it would have went. For a little side hacking project once I encountered these issues I moved on - back in the day expectations were lower for telephony and the 8 kHz aspire models and kaldi were adequate to get that "real work" done.

Sure, that's called audio super resolution, there's a few papers/projects doing that. Haven't really seen models which are robust and have good generalization though.