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.

A population perspective on mobile phone related tasks

Chapter 6 A Population Perspective on Mobile Phone Related Tasks M. Bradley, S. Waller, J. Goodman-Deane, l. Hosking, R. Tenneti, PM Langdon and PJ Clarkson 6.1 Introduction For design to be truly inclusive, it needs to take into ...

Complementary Phone Error training

This paper introduces a novel method for the training of a complementary acoustic model with respect to set of given acoustic models. The method is based upon an extension of the Minimum Phone Error (MPE) criterion and aims at producing a model that makes complementary phone errors to those already trained. The technique is therefore called Complementary Phone Error (CPE) training. The method is evaluated using an Arabic large vocabulary continuous speech recognition task. Reductions in word error rate (WER) after combination with a CPE-trained system were obtained with up to 0.7% absolute for a system trained on 172 hours of acoustic data and up to 0.2% absolute for the final system trained on nearly 2000 hours of Arabic data.

Integration of silicon plasmonic schottky photodetector for on chip signal tapping at telecom wavelengths

We experimentally demonstrate the use of an on-chip integrated Schottky plasmonic detector for testing, monitoring and tapping signals in plasmonic and photonic devices. Theoretical model and measurement of external and integrated devices will be presented. © OSA 2013.

Integration of silicon plasmonic schottky photodetector for on chip signal tapping at telecom wavelengths

We experimentally demonstrate the use of an on-chip integrated Schottky plasmonic detector for testing, monitoring and tapping signals in plasmonic and photonic devices. Theoretical model and measurement of external and integrated devices will be presented. © OSA 2013.

Integration of silicon plasmonic schottky photodetector for on chip signal tapping at telecom wavelengths

We experimentally demonstrate the use of an on-chip integrated Schottky plasmonic detector for testing, monitoring and tapping signals in plasmonic and photonic devices. Theoretical model and measurement of external and integrated devices will be presented. © OSA 2013.