MUSE CSP: An Extension to the Constraint Satisfaction Problem

TL;DR

This paper introduces MUSE CSP, an extension of the constraint satisfaction problem designed to efficiently handle multiple segmented instances sharing variables, with applications in signal processing, speech recognition, and computer vision.

Contribution

It presents the concept of MUSE CSP, including new consistency notions and algorithms, enabling compact representation and processing of multiple related CSP instances.

Findings

MUSE CSP effectively represents multiple similar CSPs with shared variables.

Algorithms for MUSE arc and path consistency are developed.

Demonstrated applications include speech recognition and sentence parsing.

Abstract

This paper describes an extension to the constraint satisfaction problem (CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem). This extension is especially useful for those problems which segment into multiple sets of partially shared variables. Such problems arise naturally in signal processing applications including computer vision, speech processing, and handwriting recognition. For these applications, it is often difficult to segment the data in only one way given the low-level information utilized by the segmentation algorithms. MUSE CSP can be used to compactly represent several similar instances of the constraint satisfaction problem. If multiple instances of a CSP have some common variables which have the same domains and constraints, then they can be combined into a single instance of a MUSE CSP, reducing the work required to apply the constraints. We introduce the concepts of MUSE node consistency, MUSE arc consistency, and MUSE path consistency. We then demonstrate how MUSE CSP can be used to compactly represent lexically ambiguous sentences and the multiple sentence hypotheses that are often generated by speech recognition algorithms so that grammar constraints can be used to provide parses for all syntactically correct sentences. Algorithms for MUSE arc and path consistency are provided. Finally, we discuss how to create a MUSE CSP from a set of CSPs which are labeled to indicate when the same variable is shared by more than a single CSP.