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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)
In this paper, we summarize systems submitted by PSTL to the evaluation. We ran Meta-Data (MD) on Switchboard (SWB) and Broadcast News (BN) data. Speech-to-text systems were built and tested on both SWB and BN systems with limited real-time constraints. For our first participation , our systems were characterized by low complexity, exploratory operating(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)
This paper presents a new speech feature representation using a wavelet decomposition of speech signal called subband analysis. This parameterization derives cepstral coefficients from the output of an unbalanced tree-structured filter-bank combining high-pass and low-pass filters with downsampling units. Inspired from the SUBCEP analysis of [1] and [2],(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)
In this paper, we present the application of eigenvoices to self-adaptation. This adaptation algorithm happens to be rather well-suited for such a task. First, it is an extremely fast adaptation algorithm, and thus well tailored to work for very short amounts of adaptation data. It is also believed to be rather more tolerant of errorful recognition. A third(More)