Improvement of Embedded Human-Machine Interfaces Combining Language, Hypothesis and Error Models

Abstract

In this paper, a generic Symbol Input Correction Method for Human-Machine Interfaces, especially useful for embedded devices where the input subsystem is often size-constrained, combining a Language Model, the Input Hypothesis information and an Error Model is proposed. The approach can be seen as a flexible and efficient way to perform Stochastic Error-Correcting Language Modeling. We use Weighted Finite-State Transducers (WFSTs) to represent the Language Model, the complete set of symbol-input Hypotheses interpreted as a sequence of vectors of symbol probabilities, and an Error Model. This approach is different to other methods since it does not involve an explicit parsing process and also because it combines the practical advantages of a de-coupled (input-system + post-processor) model with the error-recovery power of a integrated approach where the capture and the interpretation are performed in the same element (e.g. in a HMM). The symbol-input subsystem can be a physical, "soft" (touch screen-based) or reduced (as in a mobile phone) keyboard, a speech or gesture-based recognizer, an Off-line or On-line OCR system or any other Human-Machine Interface consisting of a sequence of symbols conveying information belonging to a language where the segmentation of the input is known (although some segmentation errors can be recovered by the Error Model).