Fabian Triefenbach

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Automatic speech recognition has gradually improved over the years, but the reliable recognition of unconstrained speech is still not within reach. In order to achieve a breakthrough, many research groups are now investigating new methodologies that have potential to outperform the Hidden Markov Model technology that is at the core of all present commercial(More)
Most automatic speech recognition systems employ Hidden Markov Models with Gaussian mixture emission distributions to model the acoustics. There have been several attempts however to challenge this approach, e.g. by introducing a neural network (NN) as an alternative acoustic model. Although the performance of these so-called hybrid systems is actually(More)
Thanks to research in neural network based acoustic modeling, progress in Large Vocabulary Continuous Speech Recognition (LVCSR) seems to have gained momentum recently. In search for further progress, the present letter investigates Reservoir Computing (RC) as an alternative new paradigm for acoustic modeling. RC unifies the appealing dynamical modeling(More)
Accurate acoustic modeling is an essential requirement of a state-of-the-art continuous speech recognizer. The Acoustic Model (AM) describes the relation between the observed speech signal and the non-observable sequence of phonetic units uttered by the speaker. Nowadays, most recognizers use Hidden Markov Models (HMMs) in combination with Gaussian Mixture(More)
It has been shown for some time that a Recurrent Neural Network (RNN) can perform an accurate acoustic-phonetic decoding of a continuous speech stream. However, the error back-propagation through time (EBPTT) training of such a network is often critical (bad local optimum) and very time consuming. These problems hamper the deployment of sufficiently large(More)
In this paper a formerly proposed continuous digit recognition system based on Reservoir Computing (RC) is improved in two respects: (1) the single reservoir is substituted by a stack of reservoirs, and (2) the straightforward mapping of reservoir outputs to state likelihoods is replaced by a trained non-parametric mapping. Furthermore, it is shown that a(More)
Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel(More)
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem(More)