Name
Title | ||
|---|---|---|
Composition of Constraint, Hypothesis and Error Models to improve interaction in Human-Machine Interfaces |
Abstract | ||
|---|---|---|
A method that improves the interaction in Human-Machine Interfaces is proposed.The method combines the evidences of several sources of information.Models are repesented using Weighted Finite-State Transducers (WFSTs) and composed.Models are built separately but final composed WFST keeps full error-recovery power.A significant interaction user effort can be saved when using the proposed process. Although there are many tasks where output strings are automatically generated from a set of evidence, they are not perfect and human intervention is often required to correct the result. In this paper we present a generic Symbol Input Interaction Method for Human-Machine Interfaces that combines multi-source information: an input Hypothesis Model, an Error Model, a Constraint Model and a user interaction scheme.We use Weighted Finite-State Transducers (WFSTs) to represent the different sources of information available: the initial hypotheses, the possible errors, the constraints imposed by the task (interaction language) and the user input. The fusion of these models to find the most probable output string can be performed efficiently by using carefully selected transducer operations. The proposed system initially suggests an output based on the set of hypotheses, possible errors and Constraint Models. Then, if human intervention is needed, a multimodal approach, where the user input is combined with the aforementioned models, is applied to produce, with a minimum user effort, the desired output. This approach offers the practical advantages of a de-coupled model (e.g. input-system + parameterized rules + post-processor), keeping at the same time the error-recovery power of an integrated approach, where all the steps of the process are performed in the same formal machine (as in a typical HMM in speech recognition) to avoid that an error at a given step remains unrecoverable in the subsequent steps. After a presentation of the theoretical basis of the proposed multi-source information system, its application to two real world problems, as an example of the possibilities of this architecture, is addressed. The experimental results obtained demonstrate that a significant user effort can be saved when using the proposed procedure. A simple demonstration, to better understand and evaluate the proposed system, is available on the web https://demos.iti.upv.es/hi/. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.inffus.2015.09.001 | Information Fusion |
Keywords | Field | DocType |
Multi-source information fusion,Human–machine interaction,Weighted finite-state transducer composition,Interactive multimodal string correction | Information system,Data mining,Human–machine system,Parameterized complexity,Computer science,Input hypothesis,Artificial intelligence,Hidden Markov model,Machine learning,Human machine interaction | Journal |
Volume | Issue | ISSN |
29 | C | 1566-2535 |
Citations | PageRank | References |
0 | 0.34 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Ramon Navarro-Cerdan | 1 | 18 | 3.33 |
| Rafael Llobet | 2 | 72 | 8.78 |
| Joaquim Arlandis | 3 | 85 | 9.92 |
| Juan C. Pérez-Cortés | 4 | 137 | 16.20 |