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Title: Bayesian networks for predicting duration of phones
Author: Goubanova, Olga
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2006
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The duration of a phonetic segment (phone) is usually modelled with a database of feature vectors, that consist of a set of linguistic factors’ (attributes’). There have been a number of models developed for predicting a phone’s duration, ranging from rule-based to neural nets to classification and regression tree (CART) to sums-of-products (SoP) models duration is predicted by a decision tree. In our work, we use a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the phone’s duration. Interactions between factors are represented as conditional dependency relations in this graphical model. During training, the parameters of the belief network are learned via the Expectation Maximisation (EM) algorithm. The duration of each phone in the test set is then predicted via Bayesian inference: given the parameters of the belief network, we calculate the probability of a phone taking on a particular duration given the observations of the linguistic variables. The duration value with the maximum probability is chosen as the phone’s duration. We contrasted the results of the belief network model with those of the sums of products and CART models. We trained and tested all three models on the same data. In terms of the RMS error our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. We believe our Bayesian model has many advantages compared to CART and SoP models. For instance, it captures the factors’ interactions in a concise way by causal relationships among the variables in the graphical model. The Bayesian model also makes robust predictions of phone duration in cases of missing or hidden data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available