Siri text to speech too fast
SIRI most likely does the first.Bring your Text to life with Text to Speech! Text to Speech produces natural-sounding synthesised Text from words that you have entered in. In the previous way, your voice would be built from the bits and pieces of recorded session - but here, it would be artificially generated. You build a voice model and try to produce the sound synthetically. The other way to do it is what HannasAnarion refers to below. If done well, this would sound exactly like the original speaker. You can't just put phoneme from anywhere as it would look too robotic, so you would try to find the most suitable phoneme or blocks of them to minimize the robotic effect. If it doesn't exist (more likely), you go back to basics and try to find the building blocks (called phonemes) that would makeup the text and try to put them together. Ofcourse, this would work well if what needs to be spoken exists in the database. Once you have this database, on any given input, you try to pluck words/sentences from the database and that becomes the speech. Ofcourse, you can't do that - so you make a meaningful recording session which "covers" lot of different spoken contexts. One way roughly is to record all possible words/sentences and build a huge database. For text to speech, there are two ways to do it. For speech to text, refer to HannasAnarion answer below. I am not sure if you meant speech to text or text to speech. So, we teach computers using statistical analysis of a human-transcribed corpus, so the computer can look for tiny little cues, and pick the most likely phoneme to go in that spot, and the most likely word that is being said. I have some experience with this, and ignoring the text at the bottom, I could easily tell you that there was a plosive right in the middle, but there's no way I could tell whether it's a /k/, /g/, /p/, /b/. Fricatives like /s/, /z/, and /ʃ/ are a long period of static, but it's really really hard to tell the difference between those categories. Nasals like /m/ and /n/ are only one bottom formant, and silence in the higher registers. Plosives like /d/, /k/, and /b/ are a complete stop in the signal followed by a short burst of random static. And the different classes of sounds are somewhat easy to tell apart. Vowels, fortunately, are fairly easy, as you can see, the first two thick lines (called Formants) are different for each vowel.
The trick then, is to get a machine to go from that picture, back to the speech.Īnd it's actually pretty hard. The x axis is time, the y axis is frequency, and the z axis (shown as darkness) is the amplitude at that frequency. When you perform a Fourier Transform on speech, it looks like this.
A Fourier Transform (pronounced "four-yay") is a complex mathematical algorithm that I don't understand that lets you decompose a complex wave into it's constituent simple waves. The bottom wave is the sum of the three top waves. Speech is a complex pressure wave containing lots of different waves with different amplitudes and frequencies all at the same time, additively and subtractively interfering with each other, like this. But those differences are hard for us to tell, I mean, they're just sounds, and there is no conscious step between you hearing the sound, and the phoneme(phychological representation of a sound) appearing in your head. The answer is that different speech sounds sound different (shocking, right?). I'm a linguist and I study exactly this type of thing!