Clustering of triphones using phoneme similarity estimation for the definition of a multilingual set of triphones
This paper addresses the problem of multilingual acoustic modelling for the design of multilingual speech recognisers. An agglomerative clustering algorithm for the definition of multilingual set of triphones is proposed. This clustering algorithm is based on the definition of an indirect distance measure for triphones defined as a weighted sum of the explicit estimates of the context similarity on a monophone level. The monophone similarity estimation method is based on the algorithm of Houtgast. The new clustering algorithm was tested in a multilingual speech recognition experiment for three languages. The algorithm was applied on monolingual triphone sets of language specific recognisers for all languages. In order to evaluate the clustering algorithm, the performance of the multilingual set of triphones was compared to the performance of the reference system composed of all three language specific recognisers operating in parallel, and to the performance of the multilingual set of triphones produced by the tree-based clustering algorithm. All experiments were based on the 1000 FDB SpeechDat(II) databases (Slovenian, Spanish and German). Experiments have shown that the use of the clustering algorithm results in a significant reduction of the number of triphones with minor degradation of recognition rate.
multilingual acoustic modelling speech recognition tree-based clustering speechdat(ii) databases agglomerative triphone clustering