Abstract. Constructing a student model for language tutors is a challenging task. This paper describes using knowledge tracing to construct a student model of reading proficiency and validates the model. We use speech recognition to assess a student's reading proficiency at a subword level, even though the speech recognizer output is at the level of words and is statistically noisy. Specifically, we estimate the student's knowledge of 80 letter to sound mappings, such as ch making the sound /K / in "chemistry. " At a coarse level, the student model did a better job at estimating reading proficiency for 47.2 % of the students than did a standardized test designed for the task. Although not quite as strong as the standardized test, our assessment method can provide a report on the student at any time during the year and requires no break from reading to administer. Our model's estimate of the student's knowledge on individual letter to sound mappings is a significant predictor of whether he will ask for help on a particular word. Thus, our student model is able to describe student performance both at a coarse- and at a fine-grain size.

Lowering the perplexity of a language model does not always translate into higher speech recognition accuracy. Our goal is to improve language models by learning from speech recognition errors. In this paper we present an algorithm that first learns to predict which n–grams are likely to increase recognition errors, and then uses that prediction to improve language models so that the errors are reduced. We show that our algorithm reduces a measure of tracking error by more than 24 % on unseen test data from a Reading Tutor that listens to children read aloud. 1.

In this paper, a novel architecture is proposed for the speech recognition component in a reading tutor. Decoding starts with an unconstrained phoneme recogniser that produces a phoneme lattice. Next, the best path in the lattice is looked for based on a phoneme level finite state transducer that models the words in the sentence to be read and that includes solutions for expected reading miscues and for unexpected events and disfluencies. An advantage of the architecture is its modularity as the first module is a generic phoneme recogniser while the second contains all task specific information. Moreover, the intermediate phoneme lattice adds flexibility to the system as lattice re-scoring allows, at an early stage of recognition, the incorporation of elaborate acoustic features that don’t fit in a typical HMM-based recogniser, for instance segment based features. Experiments with the proposed system show favorable reading miscue detection and false alarm rates compared to the state-of-the-art systems described in the literature. In addition we introduce an efficient VTLN system that avoids delays in the recognition which would be incompatible with the immediate feedback often needed in a reading tutor. Using the VTLN, the acoustic modelling for children between 5 and 11 years old could be improved considerably. 1.

Abstract not found

Automation of oral reading assessment and of feedback in a reading tutor is a very challenging task. This paper describes our research aiming at developing such automated systems. First topic is the recording and annotation of CHOREC, the Flemish database of children’s oral reading we develop in order to characterize oral reading processes statistically. Next, we propose a classification of both oral reading strategies and errors, which provides the basis of the envisaged assessment and feedback. Finally, experimental results show that our two-layered recognition system is able to provide high reading miscue detection rates, while only few correctly read words are erroneously tagged as miscue. Index Terms: reading assessment, database annotation, speech technology, education.

Abstract not found

The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Hagen, Andreas (Ph.D., Computer Science)

Using knowledge tracing to measure student reading proficiencies

Off-task speech is speech that strays away from an intended task. It occurs in many dialog applications, such as intelligent tutors, virtual games, health communication systems and humanrobot cooperation. Off-task speech input to computers presents both challenges and opportunities for such dialog systems. On the one hand, off-task speech contains informal conversational style and potentially unbounded scope that hamper accurate speech recognition. On the other hand, an automated agent capable of detecting off-task speech could track users’ attention and thereby maintain the intended conversation by bringing a user back on task; also, knowledge of where off-task speech events are likely to occur can help the analysis of automatic speech recognition (ASR) errors. Related work has been done in confidence measures for dialog systems and detecting out-of-domain utterances. However, there is a lack of systematic study on the type of off-task speech being detected and generality of features capturing off-task speech. In addition, we know of no published research on detecting off-task speech in children’s interactions with an automated agent. The goal of this research is to fill in these blanks to provide a systematic study of off-task speech, with an emphasis on child-machine interactions. To characterize off-task speech quantitatively, we used acoustic features to capture its

Abstract. This paper presents two new corpus-based studies of feedback in the domain of teaching Spanish as a foreign language, concentrating on the type and frequency of different feedback moves, as well as what happens in the moves that follow the feedback. In particular, as well as looking at positive feedback, it concentrates on two general kinds of negative feedback strategies: (1) Giving-Answer Strategies (GAS), where the teacher directly gives the desired target form or indicates the location of the error, and (2) Prompting-Answer Strategies (PAS), where the teacher pushes the student less directly to notice and repair their own error. Investigating the GAS/PAS distinction sheds light on the relative importance for Intelligent Computer-Assisted Language Learning (ICALL) systems of knowledge construction from interaction, which many believe is crucial for effective learning from ITS. The main finding here is that, although GAS occur more frequently than PAS in both corpora, it is the PAS that are more effective, in terms of eliciting explicit repairs by the students. The first study takes place in a classroom context, whereas the second, smaller, study looks at tutorial interactions. This makes it possible to investigate the extent to which the mode of interaction influences the frequency and effectiveness of feedback moves, as well as to look at how concepts such as “wait time ” are relevant to explain moves that are ineffective. The paper concludes by using these results to make recommendations about how to choose appropriate feedback moves in ICALL systems.