No it isn't. That just means 2-3% of your content needs to be double-checked by a person at the audio level, saving huge amounts of time - equally true of human transcription, in which individual words are often [UNINTELLIGEBLE].

Would you want to review this fully before going into court, absolutely - because you'd want to play the recording to a jury for emotional impact. Can you rely on it when you want to quickly read through hours of conversation and make decisions about whether to invest further resources (which might just mean another hour of listening back to the original audio)? Also absolutely. Bear in mind that a lot of these errors have little to no semantic impact, being on the same level as typos or misspellings in a written communication.

Bear in mind too that if law enforcement (honest or not) is so interested in you that they're willing to record your conversations, your day is already ruined, you just don't know it yet. The change here is one of scale rather than quality.

Yeah, I tried to use automated transcription for a research project and we had to do it all manually because the few errors (I would say it did pretty well given our recording quality) were often dropping words like "not", which changed the whole meaning of a sentence! It was a useful assistance during transcription, but I really hope they would verify it was correct before arresting anyone based on it.

Microsoft announced their voice transcription technology a couple years ago and were also touting ~97-98% accuracy which was actually better than human transcription error rates. The errors are usually in part people garbling their own speech, or they move their head while talking and the microphone misses a syllable. Anything in that error bar would probably fall under "reasonable doubt"

I've worked with similar technology in the law enforcement space and the software is never used to make decisions. You can make out critical timestamps in conversations and a law enforcement officer will always manually confirm the software's assessments.

I imagine a certain percentage of a given population is on a voice call at any one time.

1. Set up a computer with voice recognition software that flags certain patterns.

2. Connect computer to voice call communication network.

3. Configure computer to switch between calls every x number of seconds.

Think of it like a system to generate leads for law enforcement that can be integrated with other systems to produce the best quality leads.

Not really. Imagine that they do simple keyword matching on the text. Anything that's missed (part of the 97%) the criminals get away with. Anything that matches in the 3% is then checked by a human (by listening to the audio at that time stamp). So you only need to manually check the 3%, and even then only if something you're interested in is found.

One would think that the few crucial bits of information gleaned are listened to manually, and the machine translation is not the only thing the judge or a jury sees.

I think something similar already exists. See this, for example: https://koe.ai/recast/

Although I don't know if they're using anything similar to what you suggest. Very cool idea, anyway!

Not sure about open source, but in general, automated transcription systems need a separate track for each different speaker. So for example, for a phone call with one person on each end, you need two separate channels (recording systems usually split them left/right on one stereo file).

I use a service called sonix.ai. It's paid but I think they have a free tier or trial period, and it's not very expensive. I'm excited about this new OpenAI thing because I'd rather do it on my own hardware than send it to the cloud, but this company has earned its commercial success.