Our Work

1. Hierarchical Voting Experts: An Unsupervised Algorithm for Hierarchical Sequence Segmentation Matt Miller and Alex Stoytchev, Developmental Robotics Lab, Iowa State University [Saffran, Aslin et. al. 1996] Baby’s task: tupirogolabubedakupadoditupirotupirogolabutupiro… tupiro*golabu*bedaku*padoti*tupiro*tupiro*golabu*tupiro… [Cohen and Adams, 2001] Voting Experts’ task: itwasabrightcolddayinaprilandtheclockswere… High Boundary Entropy Low Internal Entropy Our Work i t w a s a b r i g h t … • Extend Voting Experts to more general domains • Use VE for unsupervised segmentation of hierarchically structured sequences • Improve accuracy of segmentation by using • “top down” information Votes: 4 1 6 1 3 1 7 1 i t w a s * a * b r i g h t … itwas*a*bright*cold*day*in*april*andthe*clockswere… Interesting because: Entropy metrics very similar to “Statistical cues” of Saffran, Aslin et. al. General model – useful for more than text Surprisingly effective, given its simplicity Miller and Stoytchev, ICDL 2008

2. Hierarchical Voting Experts: An Unsupervised Algorithm for Hierarchical Sequence Segmentation Experiments Other codes: • Demonstrate that HVE works • Explore the domain of applicability • Morse code • ASCII Octal • Random Code 3rd Voting Expert: • Improves Accuracy • Top-Down Information Other Experiments: • No Time “Future” Work • Tokenize an audio stream and apply HVE to find breaks • Use artificially generated and spoken audio • Results on “baby talk” and audio CD data are promising Miller and Stoytchev, ICDL 2008