This paper presents an investigation on the use of explicit statistical duration models for Cantonese connected-digit recognition. Cantonese is a major Chinese dialect. The phonetic compositions of Cantonese digits are generally very simple. Some of them contain only a single vowel or nasal segment. This makes it difficult to attain high accuracy in the automatic recognition of Cantonese digit strings. Recognition errors are mainly due to the insertion or deletion of short digits. It is widely admitted that the hidden Markov model does not impose effective control on the duration of the speech segments being modeled. Our approach uses a set of statistical duration models that are built explicitly from automatically segmented training data. They parametrically describe the distributions of various absolute and relative duration features. The duration models are used to assess recognition hypotheses and produce probabilistic duration scores. The duration scores are added with an empirically determined weight to the acoustic score. In this way, a hypothesis that is competitive in acoustic likelihood, but unfavorable in temporal
organization, will be pruned. The conventional Viterbi search algorithms for connected-word recognition are modified to incorporate both state-level and word-level duration features. Experimental results show that absolute state duration gives the most noticeable improvement in digit recognition accuracy. With the use of duration information, insertion errors are much reduced, while deletion errors increase slightly. It is also found that explicit duration models are more effective for slow speech than for fast speech.