Luca Rigazio

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Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and(More)
1. ABSTRACT The " eigenvoice " technique achieves rapid speaker adaptation by employing prior knowledge of speaker space obtained from reference speakers to place strong constraints on the initial model for each new speaker ½¼¼. It has recently been shown to yield very fast adaptation for a large-vocabulary system ¿¿ (modifies the technique in an(More)
We present an optimized implementation of the Viterbi algorithm suitable for small to large vocabulary, and isolated or continuous speech recognition. The Viterbi algorithm is certainly the most popular dynamic programming algorithm used in speech recognition. In this paper we propose a new algorithm that outperforms the Viterbi algorithm in term of(More)
In this paper we address the problem of speaker adaptation in noisy environments. We estimate speaker adapted models from noisy data by combining unsuper-vised speaker adaptation with noise compensation. We aim at using the resulting speaker adapted models in environments that differ from the adaptation environment, without a significant loss in(More)
This paper investigates the use of a large corpus for the training of a Broadcast News speech recognizer. A vast body of speech recognition algorithms and mathematical machinery is aimed at smoothing estimates toward accurate modeling with scant amounts of data. In most cases, this research is motivated by a real need for more data. In Broadcast News,(More)
In this paper, we propose an algorithm that compensates for both additive and convolutional noise. The goal of this method is to achieve an efficient environmental adaptation to realistic environments both in terms of computation time and memory. The algorithm described in this paper is an extension of an additive noise adaptation algorithm presented in(More)
Linear feature space transformations are often used for speaker or environment adaptation. Usually, numerical methods are sought to obtain solutions. In this paper, we derive a closed-form solution to ML estimation of full feature transformations. Closed-form solutions are desirable because the problem is quadratic and thus blind numerical analysis may(More)