Phone-level pronunciation scoring and assessment for interactive language learning

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilizes a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native confidence scores and on rejection statistics provided by human judges. Further improvements are obtained by incorporating models of the subject's native language and by augmenting the recognition networks to include expected pronunciation errors. The various GOP measures are assessed using a specially recorded database of non-native speakers which has been annotated to mark phone-level pronunciation errors. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring different aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its refinements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements.

SU8 bio-chemical sensor microarrays

Microfabricated cantilevers have recently attracted considerable attention as novel label-free chemical and biological biosensors which translate surface reactions into nanomechanical bending motion. However these studies have primarily focused on commercially available silicon cantilevers and relatively little work has been performed on cantilevers fabricated from other materials. Polymeric materials, offer significant advantages over silicon by virtue of the low Young's modulus, ease of microfabrication and reduced cost. In this paper, we report a non-vacuum fabrication process to produce arrays of SU8 cantilevers and demonstrate their application as chemical sensors using in situ reference cantilevers. © 2006 Elsevier B.V. All rights reserved.