Speech segmentation using regression fusion of boundary predictions

In the present work we study the appropriateness of a number of linear and non-linear regression methods, employed on the task of speech segmentation, for combining multiple phonetic boundary predictions which are obtained through various segmentation engines. The proposed fusion schemes are independent of the implementation of the individual segmentation engines as well as from their number. In order to illustrate the practical significance of the proposed approach, we employ 112 speech segmentation engines based on hidden Markov models (HMMs), which differ in the setup of the HMMs and in the speech parameterization techniques they employ. Specifically we relied on sixteen different HMMs setups and on seven speech parameterization techniques, four of which are recent and their performance on the speech segmentation task have not been evaluated yet. In the evaluation experiments we contrast the performance of the proposed fusion schemes for phonetic boundary predictions against some recently reported methods. Throughout this comparison, on the established for the phonetic segmentation task TIMIT database, we demonstrate that the support vector regression scheme is capable of achieving more accurate predictions, when compared to other fusion schemes reported so far.