Syllable Stream Segmentation Lab

How does an infant find the boundaries between words in a continuous stream of speech, with no pauses to mark where one word ends and the next begins? Saffran, Aslin & Newport's classic 1996 experiment showed that 8-month-olds can do it from pure statistics: syllables inside a word always follow each other, so their transitional probability is high, while syllables that happen to sit across a word boundary co-occur far less reliably. This simulator builds a live artificial language out of a configurable number of hidden equal-length "words", streams them together with no acoustic cues, and runs an online statistical learner that tracks transitional probability syllable by syllable and drops a segmentation boundary whenever that probability dips below a threshold you control. Alongside the live stream, a sparkline traces the raw probability signal over time and a heatmap shows the full bigram table the learner has accumulated so far - drag the stream to scrub back through what it has already heard.