truthexposer
Joined 9 years ago
Joined 9 years ago
Speech models usually use triphones, which turns out to be a huge amount of audio. This is particularly impressive because of how little data they need.
Google used their own datasets, which are most likely massive.
Even then, I don't believe the issue is with stress. I believe that the voices sound robotic because they are using, and also admitting because it makes their results impressive in some sense, very few samples, "less than a minute" they claim. Triphones are usually what speech systems are trained on. The amount of triphones (3-phoneme-grams) to cover a language's phonemic inventory is huge (50 phonemes = 50! triphones, which could mean a few hours of audio, although many will not occur within the language given the phonotactics of the language).
tell the eskimo seeing X different kinds of snow to any linguist and they will roll their eyes, debating whether or not to explain why the example is probably not only incorrect, but fabricated for the umpteenth time.
And with respect to the phonetic shifts, from a linguistic perspective, most changes of the phonetic radical involve one change of the initial sound, i.e. bilabial to interdental, dental to alveolar, voiced to unvoiced. With respect to the phonetic inventory of the language, these shifts are only one feature shifts within an old SPE framework. These small featural differences, whether conscious or not, (usually unconscious because we acquire languages during our infancy) are picked up and used by the speaker of the language to categorize words.
And while of course every language descends from another, and ancient Chinese isn't understandable to a modern Chinese speaker, there is a long tradition within the Chinese language, and change within the language hasn't been shaped by things as drastic as displacement of the linguistic community, or forced political rule under a foreign power, etc.
The Sapir-Whorf hypothesis, which underlies Arrival, has been debunked for years in academia, and the movie, if detached from that hypothesis, is more of a thought experiment on the effect of the linearity of time on human thought than a thought experiment on anything related to language itself.
First, literacy isn't completely related to the writing system. Look at Spanish speaking countries, where the alphabet is more phonetic than the English alphabet.
Second, Chinese characters that are more complex, i.e. consists of more than one radical, are usually composed with a semantic component, giving indication to the character's meaning, and a phonetic component, which gives an indication to the sound of the character. Although this isn't a rule, it helps a lot, and it's not like English doesn't have crazy non-phonetic spellings as well (how tf is "through" supposed to be pronounced for a English learner?)
Last, the Chinese language consists of MANY homophones. This isn't necessarily a bad thing, and not one out of design, but something that is the result of being one of the oldest language families in the world. It allows for the concise expression of many things using only single syllables. You might say, but what about the crazy amount of ambiguity if the language has a lot of homophones? Well, ambiguity is a huge problem in all languages and our brains seem to manage. Now, even though homophones aren't a big problem in spoken language, because of intonation and prosody giving a clue to how to analyze sentences, written language is a different story, and it would be very hard to make an easier system to handle it. For all you engineers, the fact that Chinese has characters is essentially a performance trade off. More information density for more ambiguity.