![get siri text to speech on balboka get siri text to speech on balboka](https://texttospeechrobot.com/tts-player/espeak_balabolka_online.png)
The output sounds relatively good, it is free and offers many languages and voices with lots of parameters to tweak. The most convenient solution for me was to use eSpeak from the command line. Or you could access the Google Cloud API from within R. Noam Ross tried IBM Watson’s TTS API in this post, which would be a very good solution. There are many better alternatives out there, but most of them aren’t free and/or can’t be used (as easily) from R. I uploaded the results to Soundcloud for you to hear: - audio-tts-bruce - audio-tts-theresa - audio-tts-angela - audio-tts-mchen-bruce - audio-tts-mchen-joddess - audio-tts-eng-bob - audio-tts-eng-alice - audio-tts-eng-tracyĪs you can hear, it sounds quite wonky. Lapply(speakers, function(x) tts_ITRI(content, speaker = x,ĭestfile = paste0("audio_tts_", x, ".mp3"))) speakers = c("Bruce", "Theresa", "Angela", "MCHEN_Bruce", "MCHEN_Joddess", "ENG_Bob", "ENG_Alice", "ENG_Tracy") The main TTS function is tts_ITRI() and I’m going to loop over the different voice options. Here, I’ll be using a quote from DOUGLAS ADAMS’ THE HITCHHIKER’S GUIDE TO THE GALAXY: content <- "A common mistake that people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools." And it only supports English and Chinese. The only API that works right now is **ITRI ( )**.
![get siri text to speech on balboka get siri text to speech on balboka](https://s3.studylib.net/store/data/006892706_1-01022a1b4f04fda02976ce60e64db97b-768x994.png)
It doesn’t seem very comprehensive but it does the job of converting text to speech. The only package for TTS I found was Rtts. Most of the TTS systems today still suffer from this, but this is in the process of changing: there are already a few artificial TTS systems that do sound very human. Until recently, this synthetic voice did not sound anything like a human recorded voice you could definitely hear that it was “fake”. Even imaginary or new words can easily be produced and the voices can be readily exchanged. It offers more flexibility because the collection of words isn’t limited to what has been pre-recorded by a human. If the speech has been generated by a computer, this is called formant synthesis. This search is often done with decision trees, neural nets or Hidden-Markov-Models.
![get siri text to speech on balboka get siri text to speech on balboka](https://static.wixstatic.com/media/d60c66_1f09683f3d7f4290bc38789fb46cfe77~mv2.png)
Usually, these learned segments are stored in a database (either as human voice recordings or synthetically generated) that can be searched to find suitable speech parts (Unit Selection). We call this part Natural Language Processing (NLP). The structure (e.g. the pronunciation) of these entities is then learned in context. With this method, text is first normalized and divided into smaller entities that represent sentences, syllables, words, phonemes, etc.
![get siri text to speech on balboka get siri text to speech on balboka](https://responsivevoice.org/wp-content/uploads/2015/07/text-to-speech-languages.png)
A very important method is Unit Selection synthesis. There are different ways to artificially produce speech. AI-based TTS systems can take phonemes and intonation into account. Challenges for good TTS systems are the complexity of the human language: we intone words differently, depending on where they are in a sentence, what we want to convey with that sentence, how our mood is, and so on.