Saturday, 15 August 2009

Poor Networks, Neurons and Lookaheads

Syntactic networks bear similarities to biological networks since their levels are scale-free, i.e. the distribution of nodes and edges follow a power law (e.g. social networks), and small-world, i.e. most nodes can be reached by a relatively small number of steps (e.g. social networks):

From Wikipedia [EN] [ES]

A group of researchers at the Institute of Applied Linguistics in Beijing, China tried to find similarities between semantic and syntactic networks via a statistical approach and a treebank with semantic roles. Both networks are represented by small-world and scale-free graphs but differ in hierarchical structure, k-Nearest-Neighbour correlation and semantic networks tend to create longer paths, which makes it a poorer hierarchy in comparison to syntactic networks: Statistical properties of Chinese semantic networks

Temporal fluctations in speech are easily corrected by our brain. For decades this mechanism was a mystery. Two researches of the Hebrew University of Jerusalem, Israel described how neurons adjust to decode distorted sound perfectly. Although I don't understand this very technical paper, it'll perhaps provide new algorithms for speech processing: Time-Warp-Invariant Neuronal Processing

Another improvement for speech recognition and production was achieved by the Max Plank Society which developed a new mathematical model. It's based on the look-ahead assumption, i.e. our brain tries to estimate the most probable sound-sequence based on previous information, e.g. 'hot su...' = 'sun' > 'supper': Recognizing Sequences of Sequences