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In this paper, the KALDI ASR engine adapted to Italian is described and the results obtained so far on some children speech ASR experiments are reported. We give a brief overview of KALDI, we describe in detail its DNN implementation, we introduce the acoustic model (AM) training procedure and we end describing some experiments on Italian children speech together with the final test procedures.
In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters...
This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in...
Neuromorphic computing attempts to emulate the remarkable efficiency of the human brain in vision, perception and cognition related tasks. Nanoscale devices that offer a direct mapping to the underlying neural computations have emerged as a promising candidate for such neuromorphic architectures. In this paper, a Magnetic Tunneling Junction (MTJ) has been proposed to perform the thresholding operation...
Correntropy has been successfully applied in non-Gaussian signal processing, but the superior performance achieved is depends on appropriate selection of the kernel width. How to select a proper kernel width is a crucial problem in correntropy applications. In this paper, we propose an adaptive algorithm to update the kernel width, which is set at a maximum between the absolute value of instantaneous...
Neural gas (NG) is a robust vector quantization algorithm for which a descriptive mathematical model is known. According to this model, the output configuration produced by the NG algorithm samples the input data distribution with a density that follows a power law with a magnification factor that depends on data dimensionality only. The effects of shape in the input data distribution on the NG behavior,...
We have adapted backpropagation algorithm for training multilayer perceptron classifier implemented with memristive crossbar circuits. The proposed training approach takes into account switching dynamics of a particular, though very typical, type of memristive devices and weight update restrictions imposed by crossbar topology. The simulation results show that for crossbar-based multilayer perceptron...
Redox-based resistive switching devices are an emerging class of non-volatile ultra-scalable memory and logic devices. These devices offer complex internal device physics leading to rich dynamical behavior. Memristive device models are intended to reproduce the underlying redox-based resistive switching device behavior accurately to enable proper circuit simulations. A specific feature of resistively...
Discovering an efficient representation that reflects the structure of a signal ensemble is a requirement of many Machine Learning and Signal Processing methods, and gaining increasing prevalence in sensing systems. This type of representation can be constructed by Convolutive Non-negative Matrix Factorization (CNMF), which finds parts-based convolutive representations of non-negative data. However,...
The mammalian cochlea has complicated nonlinear dynamics and exhibits various nonlinear responses to a sound stimulation. In this paper, a novel cochlea model the nonlinear dynamics of which is described by an asynchronous cellular automaton (triggered by multiple clocks) is presented. It is shown that the model can reproduce typical nonlinear responses observed in physiological measurements of a...
Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical...
Agent-oriented techniques are being increasingly used in a range of networking security applications. In this paper, we introduce FNTAE, a Federated Network Traffic Analysis Engine for real-time network intrusion detection. In FNTAE, each analysis engine is powered with an incremental learning agent, for capturing attack signatures in real-time, so that the abnormal traffics resulting from the new...
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