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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)
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)
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 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)
In the past, conventional i-vectors based on a Universal Background Model (UBM) have been successfully used as input features to adapt a Deep Neural Network (DNN) Acoustic Model (AM) for Automatic Speech Recognition (ASR). In contrast, this paper introduces Hidden Markov Model (HMM) based i-vectors that use HMM state alignment information from an ASR system(More)
We present a new model called LATTICERNN, which generalizes recurrent neural networks (RNNs) to process weighted lattices as input, instead of sequences. A LATTICERNN can encode the complete structure of a lattice into a dense representation , which makes it suitable to a variety of problems, including rescoring, classifying, parsing, or translating(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)
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)
A series of eight novel diamminetetrakis(carboxylato)platinum(IV) complexes was synthesized and characterized by multinuclear (1)H-, (13)C-, (15)N-, and (195)Pt-NMR spectroscopy. Their antiproliferative potency was evaluated in three human cancer cell lines representing ovarian (CH1), lung (A549), and colon carcinoma (SW480). In cisplatin-sensitive CH1(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)