Massively parallel computing in natural language technology

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While the best known trend in speech and language processing over the last twenty years is probably still its adoption of statistical methods to crack open a variety of previously intractable computing applications (speech recognition, machine translation, web-page search, etc.), this field has recently turn another, very different, less celebrated, but equally transformative corner. State of the art research on many of these applications can now be spotted not so much by their radically new techniques, although innovations have of course still been forthcoming, as by their radically larger **scales**. A great many machine translation groups still essentially use IBM's models from the early 1990s, but trained over half a petabyte (a megabyte of megabytes) of data. The best text-to-speech synthesizers manipulate speech signals in about the same way as their predecessors did 15 years ago, in fact, but they paste together pieces of recorded speech from databases that are tens of thousands of times larger than they were back then. Bigger now does actually go a very long way towards producing better.