Computer-based script training potentially may be an effective intervention for persons with chronic aphasia.

Three perceptual experiments were conducted to test the relative importance of vowels versus consonants to recognition of fluent speech. Sentences were selected from the TIMIT corpus to obtain approximately equal numbers of vowels and consonants within each sentence and equal durations across the set of sentences. In experiments 1 and 2, subjects listened to (a) unaltered TIMIT sentences, (b) sentences in which all of the vowels were replaced by noise, or (c) sentences in which all of the consonants were replaced by noise. The subjects listened to each sentence five times, and attempted to transcribe what they heard. The results of these experiments show that recognition of words depends more upon vowels than consonants—about twice as many words are recognized when vowels are retained in the speech. The effect was observed when occurrences of [l], [r], [w], [y] [m], and [n] were included in the sentences (experiment 1) or replaced by noise (experiment 2). Experiment 3 tested the hypothesis that vowel boundaries contain more information about the neighboring consonants than vice versa.

at Boulder My Science Tutor (MyST) is an intelligent tutoring system designed to improve science learning by elementary school students through conversational dialogs with a virtual science tutor in an interactive multimedia environment. Marni, a lifelike 3-D character, engages individual students in spoken dialogs following classroom investigations using the kit-based Full Option Science System program. MyST attempts to elicit self-expression from students; process their spoken explanations to assess understanding; and scaffold learning by asking open-ended questions accompanied by illustrations, animations, or interactive simulations related to the science concepts being learned. MyST uses automatic speech recognition, natural language processing, and dialog-modeling technologies to interpret student responses and manage the dialog. Sixteen 20-min tutorials were developed for each of 4 areas of science taught in 3rd, 4th, and 5th grades. During summative evaluation of the program, students received one-on-one tutoring via MyST or an expert human tutor following classroom instruction on the science topic, representing over 4.5 hr of tutoring across the 16 sessions. A quasi-experimental design was used to compare average learning gain for 3 groups: human tutoring, virtual tutoring, and no tutoring. Learning gain was measured using standardized assessments given to students in each condition before and after each science module. Results showed that students in both the human and virtual tutoring groups had significant learning gains relative to students in the control classrooms and that there were no significant differences in learning gains between students in the human and MyST human tutoring conditions. Both teachers and students gave high-positive survey ratings to MyST.

This article describes a comprehensive approach to fully automated assessment of children's oral reading fluency (ORF), one of the most informative and frequently administered measures of children's reading ability. Speech recognition and machine learning techniques are described that model the 3 components of oral reading fluency: word accuracy, reading rate, and expressiveness. These techniques are integrated into a computer program that produces estimates of these components during a child's 1-min reading of a grade-level text. The ability of the program to produce accurate assessments was evaluated on a corpus of 783 one-min recordings of 313 students reading grade-leveled passages without assistance. Established standardized metrics of accuracy and rate (words correct per minute [WCPM]) and expressiveness (National Assessment of Educational Progress Expressiveness scale) were used to compare ORF estimates produced by expert human scorers and automatically generated ratings. Experimental results showed that the proposed techniques produced WCPM scores that were within 3-4 words of human scorers across students in different grade levels and schools. The results also showed that computergenerated ratings of expressive reading agreed with human raters better than the human raters agreed with each other. The results of the study indicate that computer-generated ORF assessments produce an accurate multidimensional estimate of children's oral reading ability that approaches agreement among human scorers. The implications of these results for future research and near term benefits to teachers and students are discussed.Reading assessments provide school districts and teachers with critical and timely information for identifying students who need immediate help; for making decisions about reading instmction; for monitoring individual student's progress in response to instmc-

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