1. Introduction This study aims at developing an automated pronunciation scoring method for second language learners of English (Hereafter, L2 learners) using both confidence scoring and classifiers. The pronunciation errors have been detected using the confidence measure from speech recognition [Franco et al. However, the accuracy of the assessment based on the confidence scores is not always ...

This work investigates linguistically motivated features for automatically scoring a spoken picture-based narration task. Specifically, we build scoring models with features for story development, language use and task relevance of the response. Results show that combinations of these features outperform a baseline system that uses state of the art speechbased features, and that best results ar...

In this paper, we present a text evaluation system for students to improve Basque or Spanish writing skills. The system uses Natural Language Processing techniques to evaluate essays by detecting specific measures. The application uses a client-server architecture and both the interface and the application itself are multilingual. The article also explains how the system can be adapted to evalu...

This paper presents an automated sleep scoring system for neonates using hidden Markov models (HMMs) derived from features extracted from electroencephalogram (EEG) signals. The automated system can be used to score neonatal sleep in real-time and such an automated system makes it possible for a clinician to monitor a patient without an expert sleep scorer present to interpret the physiological...

Most previous research on automated speech scoring has focused on restricted, predictable speech. For automated scoring of unrestricted spontaneous speech, speech proficiency has been evaluated primarily on aspects of pronunciation, fluency, vocabulary and language usage but not on aspects of content and topicality. In this paper, we explore features representing the accuracy of the content of ...