kylebgorman

Yes, this is a discussion forum, so while you're welcome to pose ethical questions, you should also feel free to weigh in on them too ;) It's a challenging question.

Is it the mere fact of someone being aroused by children that makes child pornography offensive to society, or is it our belief that the creation of such materials necessary involves some degree of exploitation? If the latter, what about the well-documented risks of exploitation of consenting adult pornographic performers? Does access to pornography increase or decrease adults' tendencies to engage in unsanctioned sexual acts?

A speech recognizer is not an "AI".

The first author's an independent scholar so I don't think that's right.

I have the same set-up: SomaFM (and a Pi with an external hard disk) and three Squeezeboxes all in sync.

If I may, there's no real reason to break out ACL vs. NAACL vs. EMNLP, since they're all run by the ACL and one would be hard-pressed to say how the EMNLP community might differ from the ACL community at this point. And if you're doing NAACL you might want to do EACL and IJCNLP too.

I have been doing this for a long time, and FreeDB's metadata is way, way worse than MusicBrainz's, and has less coverage too.

If you find any issues in MusicBrainz metadata you can submit a correction, which will be reviewed and voted on by community members. I've done this maybe 4-5 times for my collection of about 2000 releases and in each case, my correction was quickly accepted.

Sorry but this person isn't nearly famous enough to have been "cancelled".

Get W. Sidney Allen's slim text _Vox Graeca_. It goes over not only how Ancient Greek was pronounced but also how we know. The evidence includes explicit notes from grammarians and other writers, graffiti, borrowings, puns, and later forms of Greek. Allen also has another book _Vox Latina_ which does the same thing for Latin.

"text normalization" just refers to that sort of thing. it's a subcomponent of the "front-end" of a speech synthesizer but it can also be used for speech recognition (if your training data contains things like "145" you may want to convert it to read "one hundred and forty five", for various reasons) and information extraction (perhaps you want to treat "145" and "one hundred and forty five" as the same).