Taking Advantage of Spanish Speech Resources to Improve Catalan Acoustic HMMs

At TALP, we are working on speech recognition of official languages in Catalonia, i.e. Spanish and Catalan. These two languages share approximately 80 % of their allophones. The speech databases that we have available to train HMMs in Catalan have a smaller size than the Spanish databases. This difference of size of training databases results in poorer phonetic unit models for Catalan than for Spanish. The Catalan database size is not enough to allow correct training of more complex models like triphones. The aim of this work is to find segments in Spanish databases that, used in conjunction to the Catalan utterances to train the HMM models, get an improvement of the speech recognition rate for the Catalan language. To make this selection, the following information is used: the distance between the HMM which are trained separately in Spanish and Catalan, and the phonetic attributes of every allophone. A contextual acoustic unit, the demiphone, and a state tying approach are used. This tying is done by tree clustering, using the phonetic attributes of the units and the distances between the HMM states. Different tests have been carried out by using different percentage of tied states in training simultaneously in Catalan and Spanish. In this way, Catalan models are obtained that give generally better results than the models trained only with the Catalan utterances. However, we observe from one of the tests that, when the number of gaussians is increased, that improvement becomes a loss of performance. Currently, we are working on the inclusion of additional labels to avoid that tree clustering puts in the same pool phoneme realizations that are too much different.