PAC-Bayesian approach for minimization of phoneme error rate

We describe a new approach for phoneme recognition which aims at minimizing the phoneme error rate. Building on structured prediction techniques, we formulate the phoneme recognizer as a linear combination of feature functions. We state a PAC-Bayesian generalization bound, which gives an upper-bound on the expected phoneme error rate in terms of the empirical phoneme error rate. Our algorithm is derived by finding the gradient of the PAC-Bayesian bound and minimizing it by stochastic gradient descent. The resulting algorithm is iterative and easy to implement. Experiments on the TIMIT corpus show that our method achieves the lowest phoneme error rate compared to other discriminative and generative models with the same expressive power.