Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Modelling Phonetic Context Using Head-Body-Tail Models for Connected Digit Recognition
Janienke Sturm, Eric Sanders
A2RT, Dept. Language and Speech, University of Nijmegen, The Netherlands
Both whole word modelling and context modelling have proven
to improve recognition performance for connected digit strings.
In this paper we will show that word boundary variation can be
effectively modelled by applying the Head-Body-Tail (HBT)
method as proposed by Chou et al in  and also applied by
Gandhi in . Each digit is split into three parts, representing
the beginning, middle and end of a word. The middle part - the
body - is assumed to be context-independent, whereas the first
part - the head - and the last part - the tail - incorporate
information about the preceding or subsequent digit. The results
we obtained with HBT-modelling are compared with results
obtained with whole-word models (WWM’s)  and with the
results obtained with HBT-models reported in . It is shown
that using HBT models a relative improvement over contextindependent
WWM’s of 28% on string level can be reached.
Sturm, Janienke / Sanders, Eric (2000):
"Modelling phonetic context using head-body-tail models for connected digit recognition",
In ICSLP-2000, vol.1, 429-432.