Évaluation des risques-clients

L’évaluation des risques-clients (credit scoring ou évaluation du crédit par cote) désigne aujourd'hui un ensemble d'outils financiers d’aide à la décision utilisés pour évaluer automatiquement (par un algorithme) la solvabilité d'un « tiers » ainsi que le risque de non-remboursement de prêts ou de traites d'assurance, de loyer Aux États-Unis, le crédit scoring, inclut également les vols à l'étalage.. Ces outils sont principalement utilisés aux États-Unis (depuis plus d'un siècle) puis ont été utilisés au Canada (à partir des années 1920), dans certains pays anglophones et ailleurs. Trois agences privées dominent actuellement ce secteur de la finance :

Equifax (la plus ancienne)
Experian
TransUnion

Usages Ce type d'évaluation est réalisée dans les cas suivants :

évaluation d'un client (gestion de l'exposition à un risque financier, créances douteuses par exemple) ;
référencement d'un nouveau fournisseur stratégique ;
contractualisation avec un partenaire

Hierarchical Integration of Phonetic and Lexical Knowledge in Phone Posterior Estimation

Hervé Bourlard, Hamed Ketabdar

Phone posteriors has recently quite often used (as additional features or as local scores) to improve state-of-the-art automatic speech recognition (ASR) systems. Usually, better phone posterior estimates yield better ASR performance. In the present paper we present some initial, yet promising, work towards hierarchically improving these phone posteriors, by implicitly integrating phonetic and lexical knowledge. In the approach investigated here, phone posteriors estimated with a multilayer perceptron (MLP) and short (9 frames) temporal context, are used as input to a second MLP, spanning a longer temporal context (e.g. 19 frames of posteriors) and trained to refine the phone posterior estimates. The rationale behind this is that at the output of every MLP, the information stream is getting simpler (converging to a sequence of binary posterior vectors), and can thus be further processed (using a simpler classifier) by looking at a larger temporal window. Longer term dependencies can be interpreted as phonetic, sub-lexical and lexical knowledge. The resulting enhanced posteriors can then be used for phone and word recognition, in the same way as regular phone posteriors, in hybrid HMM/ANN or Tandem systems. The proposed method has been tested on TIMIT, OGI Numbers and Conversational Telephone Speech (CTS) databases, always resulting in consistent and significant improvements in both phone and word recognition rates.

2008

Acoustic models for posterior features in speech recognition

Guillermo Aradilla

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increased speaker invariance, but unlike conventional speech features also hold some peculiarities, such that their components are non-negative and sum up to one. State-of-the-art acoustic models for ASR rely on general-purpose similarity measures like Euclidean-based distances or likelihoods computed from Gaussian mixture models (GMMs), hence, they do not explicitly take into account the particular properties of posterior-based speech features. We explore here the use of the Kullback-Leibler (KL) divergence as similarity measure in both non-parametric methods using templates and parametric models that rely on an architecture based on hidden Markov models (HMMs). Traditionally, template matching (TM)-based ASR uses cepstral features and requires a large number of templates to capture the natural variability of spoken language. Thus, TM-based approaches are generally oriented to speaker-dependent and small vocabulary recognition tasks. In our work, we use posterior features to represent the templates and test utterances. Given the discriminative nature of posterior features, we show that a limited number of templates can accurately characterize a word. Experiments on different databases show that using KL divergence as local similarity measure yields significantly better performance than traditional TM-based approaches. The entropy of posterior features can also be used to further improve the results. In the context of HMMs, we propose a novel acoustic model where each state is parameterized by a reference multinomial distribution and the state score is based on the KL divergence between the reference distribution and the posterior features. Besides the fact that the KL divergence is a natural dissimilarity measure between posterior distributions, we further motivate the use of the KL divergence by showing that the proposed model can be interpreted in terms of maximum likelihood and information theoretic clustering. Furthermore, the KL-based acoustic model can be seen as a general case of other known acoustic models for posterior features such as hybrid HMM/MLP and discrete HMM. The presented approach has been extended to large vocabulary recognition tasks. When compared to state-of-the-art HMM/GMM, the KL-based acoustic model yields comparable results while using significantly fewer parameters.

EPFL2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

Ecole Polytechnique Fédérale de Lausanne2008

Acoustic models for posterior features in speech recognition

Guillermo Aradilla

EPFL2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

Ecole Polytechnique Fédérale de Lausanne2008