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With the distribution of speech technology products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on the question of how to port LVCSR systems in a fast and efficient way. More specifically we want to estimate acoustic models for a new target language using speech data from(More)
The performance of speech recognition systems is consistently poor on non-native speech. The challenge for non-native speech recognition is to maximize the recognition performance with small amount of non-native data available. In this paper we report on the acoustic modeling adaptation for the recognition of non-native speech. Using non-native data from(More)
With the distribution of speech technology products all over the world, the fast and efficient portability to new target languages becomes a practical concern. In this paper we explore the relative effectiveness of adapting multilingual LVCSR systems to a new target language with limited adaptation data. For this purpose we introduce a polyphone decision(More)
With the distribution of speech products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on rapid transfer of LVCSR systems to other languages. In former studies we evaluated the performance if limited adaptation data is available. Particularly for very time constrained tasks and(More)
We integrated the Latent Dirichlet Allocation (LDA) approach, a latent semantic analysis model, into unsupervised language model adaptation framework. We adapted a background language model by minimizing the Kullback-Leibler divergence between the adapted model and the background model subject to a constraint that the marginalized unigram probability(More)
We propose a novel approach to cross-lingual language model and translation lexicon adaptation for statistical machine translation (SMT) based on bilingual latent semantic analysis. Bilingual LSA enables latent topic distributions to be efficiently transferred across languages by enforcing a one-to-one topic correspondence during training. Using the(More)
The sense of a preposition is related to the semantics of its dominating prepositional phrase. Knowing the sense of a preposition could help to correctly classify the semantic role of the dominating preposi-tional phrase and vice versa. In this paper , we propose a joint probabilistic model for word sense disambiguation of prepositions and semantic role(More)