SpongeBob no relation CMU Pronouncing Dictionary
Features
Available characters include GLaDOS and Wheatley from Portal, characters from Team Fortress 2, Twilight Sparkle and a number of main, secondary, and supporting characters from My Little Pony: Friendship Is Magic, SpongeBob from SpongeBob SquarePants, Daria Morgendorffer and Jane Lane from Daria, the Tenth Doctor from Doctor Who, HAL 9000 from 2001: A Space Odyssey, the Narrator from The Stanley Parable, the Wii U/3DS/Switch Super Smash Bros. Announcer (formerly), Carl Brutananadilewski from Aqua Teen Hunger Force, Steven Universe from Steven Universe, Dan from Dan Vs., and Sans from Undertale.The deep learning model used by the application is nondeterministic: each time that speech is generated from the same string of text, the intonation of the speech will be slightly different. The application also supports manually altering the emotion of a generated line using emotional contextualizers (a term coined by this project), a sentence or phrase that conveys the emotion of the take that serves as a guide for the model during inference.
Emotional contextualizers are representations of the emotional content of a sentence deduced via transfer learned emoji embeddings using DeepMoji, a deep neural network sentiment analysis algorithm developed by the MIT Media Lab in 2017. DeepMoji was trained on 1.2 billion emoji occurrences in Twitter data from 2013 to 2017, and has been found to outperform human subjects in correctly identifying sarcasm in Tweets and other online modes of communication.15.ai uses a multi-speaker model—hundreds of voices are trained concurrently rather than sequentially, decreasing the required training time and enabling the model to learn and generalize shared emotional context, even for voices with no exposure to such emotional context. Consequently, the entire lineup of characters in the application is powered by a single trained model, as opposed to multiple single-speaker models trained on different datasets. The lexicon used by 15.ai has been scraped from a variety of Internet sources, including Oxford Dictionaries, Wiktionary, the CMU Pronouncing Dictionary, 4chan, Reddit, and Twitter. Pronunciations of unfamiliar words are automatically deduced using phonological rules learned by the deep learning model.The application supports a simplified version of a set of English phonetic transcriptions known as ARPABET to correct mispronunciations or to account for heteronyms—words that are spelled the same but are pronounced differently (such as the word read, which can be pronounced as either  or  depending on its tense). While the original ARPABET codes developed in the 1970s by the Advanced Research Projects Agency supports 50 unique symbols to designate and differentiate between English phonemes, the CMU Pronouncing Dictionary's ARPABET convention (the set of transcription codes followed by 15.ai) reduces the symbol set to 39 phonemes by combining allophonic phonetic realizations into a single standard (e.g. AXR/ER; UX/UW) and using multiple common symbols together to replace syllabic consonants (e.g. EN/AH0 N). ARPABET strings can be invoked in the application by wrapping the string of phonemes in curly braces within the input box (e.g. {AA1 R P AH0 B EH2 T} to denote , the pronunciation of the word ARPABET).The following is a table of phonemes used by 15.ai and the CMU Pronouncing Dictionary:
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