Acquire the tools for understanding new architectures and algorithms of dynamical recurrent
networks (DRNs) from this valuable field guide, which documents recent forays into artificial …

This chapter presents an introduction to recurrent neural networks for readers familiar with
artificial neural networks in general, and multi-layer perceptrons trained with gradient …

This handbook is inspired by two fundamental questions:” Can intelligent learning machines
be built?” and” Can they be applied to face problems otherwise unsolvable?”. A simple …

Usually gradient descent is merely a way to find a minimum, abandoned if a more efficient
technique is available. Here we investigate the detailed properties of the gradient descent …

The ability to handle temporal variation is important when dealing with real-world dynamic
signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as …

In previous work [2], we presented a technique for automatically adapting to the rate of an
incoming signal. The idea is as follows: First, a model of the signal is built using a recurrent …

This chapter attempts to apply connectionist tools to the problem of time warping. To the
extent that connectionist methods are psychologically plausible, this also gives an attractive …

In order to account for the variety of human cognitive phenomena, computational models of
mental processing require processes that occur over different timescales. Human memory is …

본 논문식 방법을 제안하고 성능을성 인식 과정이 필요치 않으수를 구하여 음성의 속도를
측정하였다. 빠른 발성음에 대한 음성음소 길이와 측정된 음소 길이간의 비율을 이용하여 특징 …

This letter describes a robust speech recognition system for recognizing fast speech by
stretching the length of the utterance in the cepstrum domain. The degree of stretching for an …