Export Reviews, Discussions, Author Feedback and Meta-Reviews

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The authors consider the problem of learning a mixture of Hidden Markov Models. The authors first suggest using a spectral learning algorithm to learn a set of parameters for a hidden Markov model, and then provide a method for resolving the permutation ambiguity in the transition matrix to recover it's underlying block-diagonal structure. I found this paper to be very well written for the most part. The experimental results section could be fleshed out a bit. In particular the 2. The authors rely on the fact that a mixture of Hidden Markov Models can be expressed as a single HMM.