Scaled random trajectory segment models

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 di er 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 sugg...

An investigation of bimodal jet trajectory in flow through scaled models of the human vocal tract

Pulsatile two-dimensional flow through static divergent models of the human vocal folds is investigated. Although the motivation for this study is speech production, the results are generally applicable to a variety of engineering flows involving pulsatile flow through diffusers. Model glottal divergence angles of 10, 20, and 40 represent various geometries encountered in one phonation cycle. F...

Scaled Models for Nucleation ∗ Barbara

Adaptation of polynomial trajectory segment models for large vocabulary speech recognition

Segment models are a generalization of HMMs that can represent feature dynamics and/or correlation in time. In this work we develop the theory of Bayesian and maximum-likelihood adaptation for a segment model characterized by a polynomial mean trajectory. We show how adaptation parameters can be shared and adaptation detail can be controlled at run-time based on the amount of adaptation data av...

Limit distribution of the degrees in scaled attachment random recursive trees

We study the limiting distribution of the degree of a given node in a scaled attachment random recursive tree, a generalized random recursive tree, which is introduced by Devroye et. al (2011). In a scaled attachment random recursive tree, every node $i$ is attached to the node labeled $lfloor iX_i floor$ where $X_0$, $ldots$ , $X_n$ is a sequence of i.i.d. random variables, with support in [0,...