Tanja Schultz

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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)
Large vocabulary speech recognition systems traditionally represent words in terms of subword units, usually phonemes. This paper investigates the potential of graphemes acting as subunits. In order to develop context dependent grapheme based speech recognizers several decision tree based clustering procedures are performed and compared to each other.(More)
In this paper, we study robust speaker recognition in far-field microphone situations. Two approaches are investigated to improve the robustness of speaker recognition in such scenarios. The first approach applies traditional techniques based on acoustic features. We introduce reverberation compensation as well as feature warping and gain significant(More)
Oral communication is transient but many important decisions, social contracts and fact 'ndings are 'rst canied out in an oral setup, documented in written form and later retrieved. At Carnegie Mellons University s Interactive Systems Laboratories we have been experimenting with the documentation of meetings. T h s paper summarizes part of the progress that(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)
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)