  We consider crowdsourced labeling under a $d$-type worker-task specialization
model, where each worker and task is associated with one particular type among
a finite set of types and a worker provides a more reliable answer to tasks of
the matched type than to tasks of unmatched types. We design an inference
algorithm that recovers binary task labels (up to any given recovery accuracy)
by using worker clustering, worker skill estimation and weighted majority
voting. The designed inference algorithm does not require any information about
worker/task types, and achieves any targeted recovery accuracy with the best
known performance (minimum number of queries per task).
