![]() ![]() And Google's are a train wreck: a mish-mash wrapped in a muddle wrapped in a mess. ![]() But to answer those questions you need good metadata. Ditto for someone who wants to look at early-19th century French editions of Le Contrat Social, or to linguists, historians or literary scholars trying to trace the development of words or constructions: Can we observe the way happiness replaced felicity in the seventeenth century, as Keith Thomas suggests? When did "the United States are" start to lose ground to "the United States is"? How did the use of propaganda rise and fall by decade over the course of the twentieth century? And so on for all the questions that have made Google Books such an exciting prospect for all of us wordinistas and wordastri. (That's what "googling" means, isn't it?) But for scholars looking for a particular edition of Leaves of Grass, say, it doesn't do a lot of good just to enter "I contain multitudes" in the search box and hope for the best. It's well and good to use the corpus just for finding information on a topic - entering some key words and barrelling in sideways. My presentation focussed on GB's metadata - a feature absolutely necessary to doing most serious scholarly work with the corpus. All of which lends a particular urgency to the concerns about whether Google is doing this right. So whoever is in charge of the collection a hundred years from now - Google? UNESCO? Wal-Mart? - these are the files that scholars are going to be using then. There's no Moore's Law for capture, and nobody is ever going to scan most of these books again. When given a text input, the trained WaveNet model can generate the corresponding speech waveforms from scratch, one sample at a time, with up to 24,000 samples per second and seamless transitions between the individual sounds.Mark has already extensively blogged the Google Books Settlement Conference at Berkeley yesterday, where he and I both spoke on the panel on "quality" - which is to say, how well is Google Books doing this and what if anything will hold their feet to the fire? This is almost certainly the Last Library, after all. During training, the network extracts the underlying structure of the speech, such as which tones follow each other and what a realistic speech waveform looks like. The model uses a neural network that has been trained using a large volume of speech samples. Unlike most other text-to-speech systems, a WaveNet model creates raw audio waveforms from scratch. On average, a WaveNet produces speech audio that people prefer over other text-to-speech technologies. It synthesizes speech with more human-like emphasis and inflection on syllables, phonemes, and words. Most voice synthesizers (including Apple's Siri) use concatenative synthesis, in which a program stores individual phonemes and then pieces them together to form words and sentences.Ī WaveNet generates speech that sounds more natural than other text-to-speech systems. It tries to distinguish from its competitors, Amazon and Microsoft, with distinct AI features.ĭeepMind's AI voice synthesis tech is notably advanced and realistic. Google Cloud Text-to-Speech is powered by WaveNet, software created by Google's UK-based AI subsidiary DeepMind, which was bought by Google in 2014. Apps such as textPlus and WhatsApp use Text-to-Speech to read notifications aloud and provide voice-reply functionality. Some app developers have started adapting and tweaking their Android Auto apps to include Text-to-Speech, such as Hyundai in 2015. Please help improve this article by introducing citations to additional sources. Relevant discussion may be found on the talk page. This section relies largely or entirely on a single source.
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