TL;DR: In this article, a method and apparatus for automatic generation of grapheme-to-phoneme rules, used in textto-speech synthesis systems, is described, which is based on a statistical analysis of a subject dictionary.
Abstract: A computer method and apparatus provide automatic generation of grapheme-to-phoneme rules, used in text-to-speech synthesis systems. The invention method and apparatus are based on a statistical analysis of a subject dictionary. The dictionary preferably contains words and their corresponding phonemic data representations, and is analyzed for subgraph patterns. The phoneme strings for words containing the subgraph patterns are then analyzed for common phoneme substrings (subphones) associated with each subgraph. The subphones associated with each subgraph are then checked for conditions such as the highest occurrence count, the proper length, and for compatibility with both ends of the subgraph to which they are associated. A subphone matching these conditions becomes paired with the subgraph to create a rule for text-to-speech processing. Separate prefix, infix, and suffix rule sets may be generated from the invention dictionary analysis.
TL;DR: A filtering algorithm for this property is introduced and it is shown that it may be combined with a restricted branching technique for the constraint-based approach and how to implement a similar branching technique in clique-inspired algorithms is shown.
Abstract: The maximum common subgraph problem is to find the largest subgraph common to two given graphs. This problem can be solved either by constraint-based search, or by reduction to the maximum clique problem. We evaluate these two models using modern algorithms, and see that the best choice depends mainly upon whether the graphs have labelled edges. We also study a variant of this problem where the subgraph is required to be connected. We introduce a filtering algorithm for this property and show that it may be combined with a restricted branching technique for the constraint-based approach. We show how to implement a similar branching technique in clique-inspired algorithms. Finally, we experimentally compare approaches for the connected version, and see again that the best choice depends on whether graphs have labels.
TL;DR: In this article, a specification of a configuration subgraph is received and a relationship may be established between the subgraph and an element in a block diagram model, and the result may be used to configure the attribute of the element during or prior to a compilation of a dynamic portion of the block diagram.
Abstract: In an embodiment, a specification of a configuration subgraph is received. The configuration subgraph may graphically specify an attribute for an element in a block diagram model. A relationship may be established between the configuration subgraph and the element. The configuration subgraph may be evaluated to produce a result. The result may be used to configure the attribute of the element during or prior to a compilation of a dynamic portion of the block diagram model. The configuration subgraph may not be used during an execution of the dynamic portion of the block diagram model.
TL;DR: In this paper, a particular model of a class of objects is selected from a set of models of the class, wherein the class models are graphs, each graph including a plurality of vertices representing objects in the class and edges connecting the vertices.
Abstract: A particular model of a class of objects is selected from a set of models of the class, wherein the class models are graphs, each graph including a plurality of vertices representing objects in the class and edges connecting the vertices. Subsets of vertices of a selected set of graphs representing the class of objects are grouped to produce a subgraph. A set of anchor vertices is selected from the subgraph. Subgraph parameterizations are determined for the set of anchor vertices of the subgraph and the subgraph parameterizations are combined with the set of class models to identify a particular class model.
TL;DR: This work introduces a new integer linear programming formulation built on node variables only, which uses new constraints based on node-separators, and indicates that the new formulation outperforms the previous ones in terms of the running time and of the stability with respect to variations of node weights.
Abstract: The Maximum (Node-) Weight Connected Subgraph Problem (MWCS) searches for a connected subgraph with maximum total weight in a node-weighted (di)graph. In this work we introduce a new integer linear programming formulation built on node variables only, which uses new constraints based on node-separators. We theoretically compare its strength to previously used MIP models in the literature and study the connected subgraph polytope associated with our new formulation. In our computational study we compare branch-and-cut implementations of the new model with two models recently proposed in the literature: one of them using the transformation into the Prize-Collecting Steiner Tree problem, and the other one working on the space of node variables only. The obtained results indicate that the new formulation outperforms the previous ones in terms of the running time and in terms of the stability with respect to variations of node weights.