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Language model adaptation based on Machine Translation (MT) is a recently proposed approach to improve the Automatic Speech Recognition (ASR) of spoken translations that does not suffer from a common problem in approaches based on rescoring i.e. errors made during recognition cannot be recovered by the MT system. In previous work we presented an efficient implementation for MT-based language model...
Due to their advantages over conventional n-gram language models, recurrent neural network language models (rnnlms) recently have attracted a fair amount of research attention in the speech recognition community. In this paper, we explore one advantage of rnnlms, namely, the ease with which they allow the integration of additional knowledge sources. We concentrate on features that provide complementary...
Compounding is one of the most productive word formation processes in many languages and is therefore a main source of data sparsity in language modeling. Many solutions have been suggested to model compound words, most of which break the compound into its constituents and train a new model with them. In earlier work, we argued that this approach is suboptimal and we presented a novel technique that...
In this paper we present a novel clustering technique for compound words. By mapping compounds onto their semantic heads, the technique is able to estimate n-gram probabilities for unseen compounds. We argue that compounds are well represented by their heads which allows the clustering of rare words and reduces the risk of over-generalization. The semantic heads are obtained by a two-step process...
In this paper we investigate whether a layered architecture that has already proven its value for small tasks, works for a system with large lexica (400k words) and language models (5-grams) as well. The architecture was designed to decouple phone and word recognition which allows for the integration of more complex linguistic components, especially at the sub-word level. It was tested on the Dutch...
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