In-service adaptation of multilingual hidden-Markov-models

Abstract

In this paper we report on advances regarding our approach to porting an automatic speech recognition system to a new target task. In cases where there is not enough acoustic data available to allow for thorough estimation of HMM parameters it is impossible to train an appropriate model. The basic idea to overcome this problem is to create a task independent seed model that can cope with all tasks equally well. However, the performance of such a generalist model is of course lower than the performance of task dependent models (if these were available). So, the seed model is gradually enhanced by using its own recognition results for incremental online task adaptation. Here, we use a multilingual romanic/germanic seed model for a slavic target task. In tests on Slovene digits multilingual modeling yields the best recognition accuracy compared to other language dependent models. Applying unsupervised online task adaptation we observe a remarkable boost of recognition performance.