Karim Filali

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Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full(More)
In recent years there has been growing interest in discrimi-native parameter training techniques, resulting from notable improvements in speech recognition performance on tasks ranging in size from digit recognition to Switchboard. Typified by Maximum Mutual Information training, these methods assume a fixed statistical modeling structure, and then optimize(More)
It is important to produce automatic speech recognition (ASR) systems that use as few computational and memory resources as possible, especially in low-memory/low-power environments such as for personal digital assistants. One way to achieve this is through parameter quantization. In this work, we compare a variety of novel subvector clustering procedures(More)
There is fast growing research on designing energy-efficient computational devices and applications running on them. As one of the most compelling applications for mobile devices, automatic speech recognition (ASR) requires new methods to allow it to use fewer computational and memory resources while still achieving a high level of accuracy. One way to(More)
We present a generalization of dynamic Bayesian networks to concisely describe complex probability distributions such as in problems with multiple interacting variable-length streams of random variables. Our framework incorporates recent graphical model constructs to account for existence uncertainty, value-specific independence, aggregation relationships,(More)
We investigate a highly effective and extremely simple noise-robust front end based on novel post-processing of standard MFCC features on the Aurora databases. It performs remarkably well on both the Aurora 2.0 and Aurora 3.0 databases without requiring any increase in model complexity. Our experiments on Aurora 2.0 have been reported in [1]. In this paper,(More)
The modulation spectrum is a promising method to incorporate dynamic information in pattern classification. It contains important cues about the nonstationary content of a signal and yields complementary improvements when it is combined with conventional features derived from short-term analysis. Many prior modulation spectrum approaches are based on(More)
We present a probabilistic model of a user's search history and a target query reformulation. We derive a simple transitive similarity algorithm for disambiguating queries and improving history-based query reformulation accuracy. We compare the merits of this approach to other methods and present results on both examples assessed by human editors and on(More)
1 Médiastinite compliquant une cellulite cervicale à porte d'entrée dentaire: à propos d'un cas et revue de la littérature Abstract Les cellulites cervicales ou fasciites cervicales nécrosantes sont des infections des parties molles développées à partir de foyer dentaire ou pharyngé dont le risque, si elles ne sont pas reconnues précocement, est l´extension(More)
We introduce a novel framework for the expression, rapid-prototyping, and evaluation of statistical machine-translation (MT) systems using graphical models. The framework extends dynamic Bayesian networks with multiple connected different-length streams, switching variable existence and dependence mechanisms , and constraint factors. We have implemented a(More)
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