Most commercially-available speech recognizers use whole-word modellin
g techniques, with a model being trained for each word in the required
vocabulary. A much more versatile system is obtained by using sub-wor
d models, which offer the potential to build word models for any appli
cation vocabulary from a single set of trained sub-word units. Ideally
, a general database is used to train, once and for all a set of vocab
ulary-independent sub-word models for the chosen language. In the past
, systems of this type have tended to give considerably worse recognit
ion performance than whole-word systems. The problem is to choose an a
ppropriate sub-word unit, incorporating all important context effects
within all possible words while not requiring an excessive number of w
ords for training. To achieve this aim, a new type of sub-word unit (c
alled the phonicle) has been developed at the Hirst Research Centre. T
he paper provides some background by explaining the complexity of the
speech signal and briefly introducing the most successful recognition
techniques, before describing the phonicle approach in some detail. Sp
eaker-dependent isolated-word recognition experiments on six example a
pplication vocabularies (with an average vocabulary size of about 80 w
ords) have demonstrated a very low average error-rate of 0.3%. The app
roach is now being applied to speaker-independent and to connected-wor
d recognition, for which it should have many commercial applications i
n versatile voice-driven systems.