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sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to
speaker independent (SI). Experimental results show comparable high keyword recognition precision in SD models for both ANN and DTW using baseline data, i.e. no transient noise, but for the SI models, a significant drop in precision was observed for the case of DTW. Additional precision analyses present the results on how
recurrent networks. Experimental results showed that multi-Layer preception provided better classification results in conjunction with the empirical mode decomposition. It is also concluded that a small set of features is sufficient to classify galaxy images and provide a fast classification. Keywords: Hubble Sequence
network, sets of coherently appearing words (treated as factors) create tightly connected groups of neurons, hence, revealing them as attractors of the network dynamics. The found factors are eliminated from the network memory by the Hebbian unlearning rule facilitating the search of other factors. Topics related to the
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