Method of moments learning for left-to-right Hidden Markov models

We propose a method-of-moments algorithm for parameter learning in Left-to-Right Hidden Markov Models. Compared to the conventional Expectation Maximization approach, the proposed algorithm is computationally more efficient, and hence more appropriate for large datasets. It is also asymptotically guaranteed to estimate the correct parameters. We show the validity of our approach with a synthetic data experiment and a word utterance onset detection experiment.