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I wonder: did they address common ML ethics questions? Specifically: Are the ML algorithms better/worse on male than on female speech? How about different langu
by luplex 3y ago
I wonder: did they address common ML ethics questions? Specifically: Are the ML algorithms better/worse on male than on female speech? How about different languages or dialects? Are they specifically tuned for speech at all, or do they also work well for music or birdsong?
That said, the examples are impressive and I can't wait for this level of understandability to become standard in my calls.
- radarsat1 3y agoThis is an important question. However, I'd like to point out that similar biases can easily exist for non-ML, hand-tuned algorithms. Even in the latter case test sets and often even "training" and "validation" sets are used for finding good parameters. Any of these can be a source of bias, as can the ears of evaluators making these decisions. It's true that bias questions often come up in ML context because fundamentally these algorithms do not work without data, but _all_ algorithms are designed by people, and _many_ can involve data in setting their parameters. Both of which can be sources of bias. ML is more known for it, I believe, because the _inductive_ biases are less than in traditional algorithms, and therefore are more keen to adopt biases present in the dataset.
- thomastjeffery 3y agoUsually regular algorithms aren't generating data that pretends to be raw data. That's the significant difference here.
- shwaj 3y agoCan you precisely define what you mean by "generating" and "pretends", in such a way that this neural network does both these things, but a conventional modern audio codec doesn't? "Pretends" is a problematic choice of words, because it anthropomorphizes the algorithm. It would be more accurate and less misleading to replace "pretends to be" with "approximates". But then it wouldn't serve your goal of (seeming to) establish a categorical difference between this approach and "regular algorithms", because that's what a regular algorithm does too. I apologize, because the above might sound rude. It's not intended to be.
- thomastjeffery 3y agoI was avoiding the word "approximate", because that implies a connection to the original raw data. A generative model guesses what data should be filled in, based on what is present in its own model. This process is totally ignorant of the original (missing) data. To contrast, a lossy codec works directly with the original data. It chooses what to throw out based on what the algorithm itself can best reproduce during playback. This is why you should never transcode from one lossy codec to another: the holes will no longer line up with the algorithm's hole-filling expectations.
- deleted 3y ago[deleted]
- samus 3y agoNot really. Any lossy codec is generating data that pretends to be close to the raw data.
- thomastjeffery 3y agoYes, but the holes were intentionally constructed such that the end result is predictable. There is a difference between pretending to be the original raw data, and pretending to be whatever data will most likely fit.
- samus 3y agoAnd that's why Packet Loss Concealment is only used to fill in the occasional lost packet. That way, the occasional vowel or the end of a syllable could be bridged over. Other improvements exist to prevent packet loss in the first place that are much less in make-samples-up territory.
- thomastjeffery 3y agoYes, I agree that this use case is entirely reasonable. I also agree that it's reasonable to be concerned in the first place.
- MauranKilom 3y agoAs a notable example, the MP3 format was hand-tuned to vocals based on "Tom's Diner" (i.e. a female voice). It has been accused of being biased towards female vocals as a result.
- jmvalin 3y agoQuoting from our paper, training was done using "205 hours of 16-kHz speech from a combination of TTS datasets including more than 900 speakers in 34 languages and dialects". Mostly tested with English, but part of the idea of releasing early (none of that is standardized) is for people to try it out and report any issues. There's about equal male and female speakers, though codecs always have slight perceptual quality biases (in either direction) that depend on the pitch. Oh, and everything here is speech only.
- unixhero 3y agoWhy is the ethics question important? It is a new feature for an audio codec, not a new material to teach in your kids curriculum.
- The_Colonel 3y agoImagine you release a codec which optimizes for cis white male voice, every other kind of voice has perceptibly lower fidelity (at low bitrates). That would not go well...
- shrubble 3y ago[flagged]
- p1esk 3y agoAn ML model might be able to, even if you can’t.
- GaggiX 3y agoAchieving the gaydar, do not give this technology to Saudi Arabia.
- Edman274 3y agoWhat people have historically called a "gay lisp" is actually a hyper-articulation of /s/ or /z/, and as you might expect, /s/ and /z/ have a lot of high frequency sounds in them. Weird as it sounds there is a possible scenario where an audio codec does a worse job reproducing the audio content of gay male speech compared to straight male speech.
- stevage 3y agohttps://en.wikipedia.org/wiki/Gay_male_speech?wprov=sfla1 https://en.wikipedia.org/wiki/Gay_male_speech?wprov=sfla1 HTH.
- panzi 3y ago
- kolinko 3y agoAs a person from a different language/accent who has to deal with this on a regular basis - having assistants like Siri not understand what I want to say, even though native speakers don't have such problem... Or before an advent of UTF - websites and apps ignoring special characters usable in my language. I wouldn't consider this a matter of ethics, and more of a technology limitations or ignorance.