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Rest In Peas — the Death of Speech Recognition
An anonymous reader writes "Speech recognition accuracy flatlined years ago. It works great for small vocabularies on your cell phone, but basically, computers still can't understand language. Prospects for AI are dimmed, and we seem to need AI for computers to make progress in this area. Time to rewrite the story of the future. From the article: 'The language universe is large, Google's trillion words a mere scrawl on its surface. One estimate puts the number of possible sentences at 10^570. Through constant talking and writing, more of the possibilities of language enter into our possession. But plenty of unanticipated combinations remain, which force speech recognizers into risky guesses. Even where data are lush, picking what's most likely can be a mistake because meaning often pools in a key word or two. Recognition systems, by going with the "best" bet, are prone to interpret the meaning-rich terms as more common but similar-sounding words, draining sense from the sentence.'"
IBM closed many of their speech research offices 1-2 years ago and transferred most of the research/data to Nuance's Dragon Naturally Speaking research.
Full Disclosure: I work for Nuance
I think you're probably about 10-20 years out of date with your criticism. AI these days is *all about* statistical machine learning which is *all about* data and not about formal or expert systems at all. This is what Google and others are doing. The AI you are describing is from the late 80s and early 90s.
Neural networks are part of the story, but many of the ideas from ANNs have been improved upon when more structured settings are available. There is actually a resurgence right now in deep neural network though.