Reading proficiency is a fundamental component of language competency. However, finding topical texts at an appropriate reading level for foreign and second language learners is a challenge for teachers. This task can be addressed with natural language processing technology to assess reading level. Existing measures of reading level are not well suited to this task, but previous work and our own pilot experiments have shown the benefit of using statistical language models. In this paper, we also use support vector machines to combine features from traditional reading level measures, statistical language models, and other language processing tools to produce a better method of assessing reading level.

N-gram language modeling typically requires large quantities of in-domain training data, i.e., data that matches the task in both topic and style. For conversational speech applications, particularly meeting transcription, obtaining large volumes of speech transcripts is often unrealistic; topics change frequently and collecting conversational-style training data is time-consuming and expensive. In particular, new topics introduce new vocabulary items which are not included in existing models. In this work, we use a variety of data sources (reflecting different sizes and styles), combined using mixture n-gram models. We study the impact of the different sources on vocabulary expansion and recognition accuracy, and investigate possible indicators of the usefulness of a data source. For the task of recognizing meeting speech, we obtain a 9% relative reduction in the overall word error rate and a 61% relative reduction in the word error rate for "new" words added to the vocabulary over a baseline language model trained from general conversational speech data.

Students bring prior knowledge and experiences to the classroom. According to the constructivist learning theory, students incorporate new knowledge into their existing knowledge frameworks. We used Classroom Assessment Techniques in an information technology course to elicit the construction of knowledge process. We found that CATs and instructor feedback can help shape and reveal this construction process. For example, responses to CATs revealed students' understandings of variables, digital representation, and iteration in the information technology domain. Some students claimed that the CATs helped put new ideas into their own words and helped them simplify the concepts.

Collecting sufficient language model training data for good speech recognition performance in a new domain is often difficult. However, there may be other sources of data that are matched in terms of topic or style, if not both. This paper looks at the use of text normalization tools to make these data more suitable for language model training, in conjunction with mixture models to combine data from different sources. We specifically address the task of recognizing meeting speech, showing a small reduction in word error rate over a baseline language model trained from conversational speech data.