A long deep and wide artificial neural net (LDWNN) with multiple ensemble neural nets for individual frequency subbands is proposed for robust speech recognition in unknown noise. It is assumed that the effect of arbitrary additive noise on speech recognition can be approximated by white noise (or speech-shaped noise) of similar level across multiple frequency subbands. The ensemble neural nets...

A major challenge in performing pattern recognition with neural networks is large input data sets; for example, high-resolution static images. There is a direct relationship between the number of inputs and the number of neurons and links required to process those inputs. Specifically, as the number of inputs increases linearly, the complexity of the neural net increases exponentially. We prese...

HIS REPORT DESCRIBES the construction of a dynamic optical hybrid system for implementing multi-layer neural networks. The communication between neurons is performed by amplitude modulating optical signals with dynamic transmission filters realized with a ferroelectric liquid crystal spatial light modulator (FLC-SLM). A large part of the information processing is thus performed in parallel. The...

In this paper, we introduced a new framework of speech recognizer based on HMM and neural net. Unlike the traditional hybrid system, the neural net was used as a post processor, which classify the speech data segmented by HMM recognizer. The purpose of this method is to improve the top-choice accuracy of HMM based speech recognition system in our lab. Major issues such as how to use the segment...

Though the time-delay neural net architecture has been recently used in a number of speech recognition applications, it has the problem that it can not use longer temporal contexts because this increases the number of connection weights in the network. This is a serious bottleneck because the use of larger temporal contexts can improve the recognition performance. In this paper, a time-derivari...