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Baby robot learns first word from human teacher
AT FIRST it's just noise: a stream of incoherent sounds, burbling away. But, after a few minutes, a fully formed word suddenly emerges: "red". Then another: "box". In this way, a babbling robot learns to speak its first real words, just by chatting with a human.
Ba, ba, ba, brick (Image: Pete Stevens) |
Between the ages of 6 and 14 months children move from babbling strings of syllables to uttering actual words. It's a necessary step en route to acquiring full language. Once a few "anchor" words have been established, they provide clues as to where words may start and finish and so it becomes easier for a child to learn to speak.
Inspired by this process, a team led by computer scientist Caroline Lyon at the University of Hertfordshire, UK, programmed their iCub humanoid robot, called DeeChee, with almost all the syllables that exist in English – around 40,000 in total. This allowed it to babble rather like a baby, by arbitrarily stringing syllables together.
The researchers also enlisted 34 people to act as teachers, who were told to treat DeeChee as if it were a child. DeeChee took part in an 8-minute dialogue with each teacher. Between each session, its memory was saved, wiped and reset, so that the experiment started anew with each teacher. At the outset of each dialogue, each of the syllables in DeeChee's lexicon had an identical score.
The researchers also enlisted 34 people to act as teachers, who were told to treat DeeChee as if it were a child. DeeChee took part in an 8-minute dialogue with each teacher. Between each session, its memory was saved, wiped and reset, so that the experiment started anew with each teacher. At the outset of each dialogue, each of the syllables in DeeChee's lexicon had an identical score.
This learning by imitation was then reinforced by encouraging remarks from the teacher when DeeChee spoke a recognisable word. DeeChee was programmed to detect these comments and give extra points to the syllables that preceded the teacher's approval. Inevitably, some nonsense syllables would get extra points too. But as this process was repeated, only those syllables that made up words would keep showing up in strings that gained approval.
Though the robot was still uttering nonsense syllables, towards the end of the 8 minutes, real words kept popping up more often than if DeeChee were still selecting syllables at random.
Though the robot was still uttering nonsense syllables, towards the end of the 8 minutes, real words kept popping up more often than if DeeChee were still selecting syllables at random.
Right now, DeeChee's speech is a far cry from full-blown language, but starting from babble could be the best way to create robots that speak naturally.
Journal reference: PLoS One, DOI: 10.1371/journal.pone.0038236
Site reference : http://www.newscientist.com/
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