Word juncture modelling based on the TIMIT database

acoustic signal which simply represents the sequence of the actually pronounced phones. This might in turn reduce recognition accuracy. One solution to this problem is to use "phonological rules" ([2], for both French and English, and [3]) which account for various pronunciation variations in the language. However, it is hard to obtain reliable sets of such rules. Because of the compactness of such rules, many variation phenomena cannot be covered. In this study, we develop data-based word juncture models, which account for the pronunciation variations at word boundaries, as an optional form of phonological rules. We used the American English TIMIT database. Issues in generating the models and using them in a continuous recognition task are discussed. A comparison is given between the coverage of the pronunciation variations by the models and by a set of phonological rules. There is a fairly good agreement between the models and the rules in predicting the pronunciation variations, whereas the models cover a larger set of variation phenomena. Furthermore, use of the models improved recognition performance. In this study, we propose a different approach. We only concentrate on the pronunciation variations at word junctures, although generally within-word deviations may also occur. We directly generate word-juncture models based on the statistics of the pronunciation variations from a training data set. Then we use these models to predict those actual phone sequences deviating from the normative sequences. The actual phone sequences are used in word recognition. The whole process of generating and using the models is illustrated in Figure 1.