Duration modeling with hidden Markov models

Abstract

In hidden Markov modeling (HMM) of speech signals, the statistics of speech characteristics are represented by HMM parameters after the HMM training. This procedure is purely statistical. This study concerns the incorporation of explicit knowledge into the HMM training. Therefore one specific parameter, i.e., segment duration, was selected. In order to study the relation between duration and HMM modeling, three types of duration PDFs (DPDFs) are distinguished: (A) the DPDF defined by the segmented database used (the actual duration histogram); (B) the DPDF defined by the trained Markov model (i.e., by the transition matrix), and (C) the DPDF based on the HMM segmentation. While PDF (A) is based on data and PDF (B) is based on the trained model, PDF (C) combines both features and is based on the available set of observation sequences. First, an explicit relation is formulated between topology of the PLU, the three DPDFs, and the so‐called Pade expansion. By using the generating function of the PDPT, it is ...