Phoneme Alignment using Large Margin Techniques
We propose an alignment method which is based on recent advances in kernel machines and large margin classifiers for sequences [13, 12], which in turn build on the pioneering work of Vapnik and colleagues [15, 4]. The alignment function we devise is based on mapping the speech signal and its phoneme representation along with the target alignment into an abstract vector-space. Building on techniques used for learning SVMs, our alignment function distills to a classifier in this vector-space which is aimed at separating correct alignments from incorrect ones. We describe a simple iterative algorithm for learning the alignment function and discuss its formal properties. Experiments with the TIMIT corpus show that our method outperforms the best performing HMM-based approach [1].