The team told volunteers, who were varied in age, occupation, gender, experience with children and familiarity with computers, to talk to DeeChee exactly how they would if they wanted to teach a real child the words for colors and patterns.
DeeChee, in turn, was programmed to hear the teacher’s speech as small units of speech called phenomes, not syllables. To DeeChee, the phrase “a red box” might contain any of the following phenomes: a, ar, re, red, e, ed, bo, box, o, ox.
By listening for and responding to praise in response to its babble, DeeChee attempted to piece together what words the teachers were trying to get it to say.
What was interesting about these experiments was not only whether or not DeeChee would succeed at learning words, but also how the volunteers themselves varied in their abilities.
“We wanted to explore human-robot interaction and were deliberately not prescriptive,” explain the authors. “However, leaving participants to talk naturally opened up possibilities of a wide range of behaviour, possibilities that were certainly realized.”
Like in a real teaching setting, the teachers – and DeeChee’s learning – varied. “Some participants were better teachers than others: some of the less good produced very sparse utterances, while other talkative participants praised DeeChee whatever it did, which skewed the learning process towards non-words.”
Overall, though, DeeChee learned. As you can see in the video, the robo-baby was able to pick up simple, one-syllable words like red, green, and heart. DeeChee’s success suggests that similar mechanisms may explain how human babies learn to talk.