Scaled random segmental models

We present the concept of a scaled random segmental model, which aims to overcome the modeling problem created by the fact that segment realizations of the same phonetic unit differ in length. In the scaled model the variance of the random mean trajectory is inversely proportional to the segment length. The scaled model enables a Baum-Welch type parameter reestimation, unlike the previously suggested, non-scaled models, that require more complicated iterative estimation procedures. In experiments we have conducted with phoneme classification, it was found that the scaled model shows improved performance compared to the non-scaled model