Prosodic control of unit-selection speech synthesis: A probabilistic approach

One problem in concatenative speech synthesis is how to incorporate prosodic factors in the unit selection. Imposing a predicted prosodic target is error-prone and does not benefit from the prosodic variability of the database. In this paper, we assume that several prosodic contours exist in the database for a same symbolic entry. This variability is represented by probabilistic models of the prosodic contours and the optimal sequence of units is searched by maximizing a joint likelihood at both segmental and prosodic levels. A generalized Viterbi algorithm is used to take into account the long-term dependencies introduced by the prosodic models. This method has been implemented in a unit selection synthesizer using an expressive speech database and a subjective experiment shows an improvement of the speech naturalness compared to a conventional unit-selection method.