Björn Hoffmeister

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We announce the public availability of the RWTH Aachen University speech recognition toolkit. The toolkit includes state of the art speech recognition technology for acoustic model training and decoding. Speaker adaptation, speaker adaptive training , unsupervised training, a finite state automata library, and an efficient tree search decoder are notable(More)
In this work, the RWTH automatic speech recognition systems developed for the third TC-STAR evaluation campaign 2007 are presented. The RWTH systems make systematic use of internal system combination, combining systems with differences in feature extraction, adaptation methods, and training data used. To take advantage of this, novel feature extraction(More)
In this paper we present a novel ASR system combination technique able to combine systems producing word graphs of different structure and with different segmentations. The new method is based on the definition of a time frame-wise word error cost function in a minimum Bayes risk framework. In contrast to confusion network combination (CNC), it preserves(More)
In this work, investigations in the course of the developement of RWTH automatic speech recognition systems developed for the second TC-STAR evaluation campaign 2006 are presented. The systems were designed to transcribe parliamentary speeches taken from the European Parliament Plenary Sessions (EPPS) in European English and Spanish, as well as speeches(More)
We evaluate system combination techniques for automatic speech recognition using systems from multiple sites who participated in the TC-STAR 2006 Evaluation. Both lattice and 1-best combination techniques are tested for cross-site and intra-site tasks. For pairwise combinations the lattice based approaches can outperform 1-best ROVER with confidence scores,(More)
This paper describes the current improvements of the RWTH Mandarin LVCSR system. We introduce a new reduced toneme set developed at RWTH. Since we are using different toneme sets and pronunciation lexica for the systems we will show a fast way to transform word lattices between systems using different toneme sets and pronunciation lexica. Finally, these(More)
We present an improved system combination technique, ˙ ıROVER. Our approach obtains significant improvements over ROVER, and is consistently better across varying numbers of component systems. A classifier is trained on features from the system lattices, and selects the final word hypothesis by learning cues to choose the system that is most likely to be(More)
We show how ROVER and confusion network combination (CNC) can be improved with classification. The general idea of improving combination with classification is that each word is assigned to a certain location and at each location a classifier decides which of the provided alternatives is most likely correct. We investigate four variations of this idea and(More)
This paper describes the current improvements of the RWTH Mandarin LVCSR system. We introduce vocal tract length nor-malization for the Gammatone features and present comparable results for Gammatone based feature extraction and classical feature extraction. In order to benefit from the huge amount of data of 1600h available in the GALE project we have(More)
This paper describes the development of the RWTH Mandarin LVCSR system. Different acoustic front-ends together with multiple system cross-adaptation are used in a two stage decoding framework. We describe the system in detail and present systematic recognition results. Especially, we compare a variety of approaches for cross-adapting to multiple systems.(More)