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Jalalvand, Azarakhsh; Triefenbach, Fabian; Verstraeten, David; Martens, Jean-Pierre (2011)
Publisher: International Speech Communication Association (ISCA)
Languages: English
Types: Conference object
Subjects: Technology and Engineering, Neural Networks, Echo State Networks, Digit Recognition, Speech Recognition, Reservoir Computing
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 quite good, their training is often problematic and time consuming. By using a reservoir – this is a recurrent NN with only the output weights being trainable – we can overcome this disadvantage and yet obtain good accuracy. In this paper, we propose the first reservoir-based connected digit recognition system, and we demonstrate good performance on the Aurora-2 testbed. Since RC is a new technology, we anticipate that our present system is still sub-optimal, and further improvements are possible.

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