7 ms·

>a specific vowel could best be represented by floating-point numbers that are culturally chosen somewhat arbitrary by language evolution). Like /ɪ/ might be 0.

>a specific vowel could best be represented by floating-point numbers that are culturally chosen somewhat arbitrary by language evolution). Like /ɪ/ might be 0.82 closeness, 0.23 backness, 0.05 roundedness in some language, but 0.79 closeness, 0.26 backness, and 0.1 roundedness in another. that's not 3 floats worth of data, those are all ints over 100. We generally think of people, but not languages, as having deep or squeaky voices. Furthermore we usually have no problem following when the speakergoesfaster or enunciates reaaaallllyy slooowwwlllyy or even ish they slur theirr wurds abit. I don't think a language specific absolute value of vowel tone is a real thing. Rather, what matters is that the direction and magnitude of the shifts in tone are all in consistent proportions relative to each other. When you look at the vocalizations of humans or even other mammals on the spectrogram there's very clearly just simple modulations happening, like moving linearly from a starting frequency to the ending frequency, or at most taking a x^2 curve to get there. When you think about it this makes sense as those sorts of patterns are trivial to implement on the hardware. Just squeeze or release the muscle on the vocal cord harder. You're right in principle any set of frequencies can be the vowels but every language is going to discretize down to a fixed number. So instead of having or needing language specific absolute "vowel frequencies", you can just look for k distinct levels in the individual speakers voice. If you've gotten as far as calculating the spectrogram, everything I described can be easily implemented by applying curve fitting to that spectrum and then thresholding the coefficients

notahacker 4y ago
I think the other thing that matters is that the relative proportions of vowel tones correspond to a word the listener expects to hear given the sentence/situation. A word pronounced so unconventionally it's been turned into a homophone for another word will still generally be understood, even if the listener notices the "wrong" vowel tone. Which is where the 2bn parameters comes in...
- IIAOPSW 4y ago
  I fully agree that context the semantic content itself primes the listenerrs expectations of the next rabbit. But that sort of language model need not be GPT-level good. It just needs to identify which words/letters are unlikely to be next to each other and which ones they might be mistaken for. An unsophisticated Markov chain could do that. Is this really a 2B parameter problem, or do we do things this way because parameters are currently cheaper than expertise?
schoen 4y ago
> You're right in principle any set of frequencies can be the vowels but every language is going to discretize down to a fixed number. So instead of having or needing language specific absolute "vowel frequencies", you can just look for k distinct levels in the individual speakers voice. But if a native English speaker speaks Japanese with five appropriately-spaced English vowels, it will be directionally correct and comprehensible, but native Japanese speakers would notice that those aren't the normal vowels used by native speakers in Japanese. So I guess I'm wondering about how much leeway there is in what sounds like a correct vowel to a native speaker, how much range there is in a given person's production, how much range there is in production among members of a community who interact with each other constantly, and how small a difference between vowels someone can detect as a listener.
- IIAOPSW 4y ago
  Here is my rank conjecture. Part of the advantage to working in frequency space as I suggest is because the output is invariant under certain transformations (up to a multiplication by a phase factor e^(i omega)). In the 2d image case, the invariant is translations along either axis. In the 1d time series case, the invariant is translations along the time axis. That's just a fancy way to say you get the same frequencies out no matter what time you pressed record. So I conjecture that the sorts of changes to the sound wave form which don't get in the way of our comprehension are exactly the invariant of some transform similar to Fourier which happens as a low level processing step shortly after the ear. The exact extent to which a vowel tone differs from the native speaker average manifests as (and is encoded in) a phase shift on the output of the transform, but the output magnitudes stay invariant and thus recognizable.

notahacker 4y ago

I think the other thing that matters is that the relative proportions of vowel tones correspond to a word the listener expects to hear given the sentence/situation. A word pronounced so unconventionally it's been turned into a homophone for another word will still generally be understood, even if the listener notices the "wrong" vowel tone. Which is where the 2bn parameters comes in...

IIAOPSW 4y ago
I fully agree that context the semantic content itself primes the listenerrs expectations of the next rabbit. But that sort of language model need not be GPT-level good. It just needs to identify which words/letters are unlikely to be next to each other and which ones they might be mistaken for. An unsophisticated Markov chain could do that. Is this really a 2B parameter problem, or do we do things this way because parameters are currently cheaper than expertise?

schoen 4y ago

> You're right in principle any set of frequencies can be the vowels but every language is going to discretize down to a fixed number. So instead of having or needing language specific absolute "vowel frequencies", you can just look for k distinct levels in the individual speakers voice. But if a native English speaker speaks Japanese with five appropriately-spaced English vowels, it will be directionally correct and comprehensible, but native Japanese speakers would notice that those aren't the normal vowels used by native speakers in Japanese. So I guess I'm wondering about how much leeway there is in what sounds like a correct vowel to a native speaker, how much range there is in a given person's production, how much range there is in production among members of a community who interact with each other constantly, and how small a difference between vowels someone can detect as a listener.

IIAOPSW 4y ago
Here is my rank conjecture. Part of the advantage to working in frequency space as I suggest is because the output is invariant under certain transformations (up to a multiplication by a phase factor e^(i omega)). In the 2d image case, the invariant is translations along either axis. In the 1d time series case, the invariant is translations along the time axis. That's just a fancy way to say you get the same frequencies out no matter what time you pressed record. So I conjecture that the sorts of changes to the sound wave form which don't get in the way of our comprehension are exactly the invariant of some transform similar to Fourier which happens as a low level processing step shortly after the ear. The exact extent to which a vowel tone differs from the native speaker average manifests as (and is encoded in) a phase shift on the output of the transform, but the output magnitudes stay invariant and thus recognizable.