Integration of context-dependent durational knowledge into HMM-based speech recognition

2. DPDF OF STANDARD HMM This paper presents research on integrating context-dependent durational knowledge into HMM-based speech recognition. The first part of the paper presents work on obtaining relations between the parameters of the context-free HMMs and their durational behaviour, in preparation for the context-dependent durational modelling presented in the second part. Duration integration is realised via rescoring in the post-processing step of our N-best monophone recogniser. We use the multi-speaker TIMIT database for our analyses. The single-state dpdf (which is geometrical) is less important than the dpdf of the whole HMM, because in actual practice it is the latter that models a phonetic segment. In this section we firstly derive the closed-form whole-model dpdf for general leftto-right HMM. Left-to-right is by far the most common type of transition topology used for speech recognition. It may include any number of skipping transitions and parallel paths but no feedback loops that contains more than one state. Then an analysis will be given of the general properties of the dpdf, with the help of some examples of useful topologies.