Commit f426a639 authored by Antoine Caubrière's avatar Antoine Caubrière
Browse files

C2 - mise en forme

parent ab6c4d79
......@@ -126,13 +126,13 @@ Le nombre optimal de gaussienne doit être déterminé empiriquement.
L'exploitation de réseau de neurones comme alternative aux GMM a tout d'abord été proposée sous forme de MLP~[\cite{bourlard1987multilayer}], puis des réseaux plus complexes les ont remplacés [\cite{ma1990tdnn}].
Toutefois, ce n'est que récemment que les DNN ont surpassé les GMM pour l'estimation des probabilités d'émission des états HMM~[\cite{hinton2012deep}].
De plus, les réseaux de neurones à retardement (\textit{Time-Delay Neural Network, TDNN}) sont particulièrement adaptés à la structure dynamique de la parole~[\cite{waibel1989phoneme}].
Ils ont ainsi permis, plus récemment, d'atteindre les performances à l'état de l'art avec une approche Markovienne~[\cite{peddinti2015time}].
De plus, les réseaux de neurones à retardement (\textit{Time-Delay Neural Network, TDNN}) sont particulièrement adaptés à la structure dynamique de la parole~[\cite{waibel1989phoneme}].
Ils ont ainsi permis d'atteindre les performances à l'état de l'art avec une approche Markovienne~[\cite{peddinti2015time}].
Concrètement, l'objectif du système neuronal exploité est de modéliser les probabilités a posteriori des états des HMM.
Dans le cadre des HMM-DNN, l'estimation des paramètres du DNN s'effectue après l'optimisation d'un modèle HMM-GMM par remplacement de la partie GMM.
Nous fournissons dans la figure~\ref{fig:hmm-dnn}, l'exemple d'une architecture HMM-DNN pour la reconnaissance de la parole.
Nous fournissons en figure~\ref{fig:hmm-dnn}, l'exemple d'une architecture HMM-DNN pour la reconnaissance de la parole.
\begin{figure}[htbp]
\begin{center}
......@@ -264,7 +264,7 @@ L'algorithme de \textit{Beam Search}, ou algorithme de recherche en faisceau, vi
Ce nombre $n$ est aussi appelé largeur du faisceau.
Avec une largeur infinie, cet algorithme est équivalent à un parcours en largeur d'un
arbre.
Pendant l'exploration de l'arbre, il s'agit de classer les successeurs du noeud courant et de sélectionner les $n$ meilleurs, suivant un score.
Lors du parcours de l'arbre, il s'agit de classer les successeurs du noeud courant et de sélectionner les $n$ meilleurs suivant un score.
Cet algorithme est couramment exploité comme stratégie de recherche au sein des sorties de systèmes séquences à séquences.
L'intérêt étant de construire un arbre des possibilités à l'aide des séquences de sorties immédiates d'un système et d'un modèle de langage.
......
......@@ -354,6 +354,9 @@
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......@@ -371,15 +374,15 @@
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......@@ -391,9 +394,16 @@
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......@@ -405,16 +415,10 @@
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......@@ -440,10 +444,6 @@
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......@@ -479,23 +479,26 @@
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Per-cep-tron, MLP\T1/jkp/m/n/10.95 ) [[][]\T1/jkp/m/sc/10.95 Ben-gio\T1/jkp/m/n
......@@ -3051,16 +3048,24 @@ Per-cep-tron, MLP\T1/jkp/m/n/10.95 ) [[][]\T1/jkp/m/sc/10.95 Ben-gio\T1/jkp/m/n
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Chapitre 3.
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