Disfluency Detection for Spoken Learner English
One of the challenges for computer aided language learning (CALL) is providing high quality feedback to learners. An obstacle to improving feedback is the lack of labelled training data for tasks such as spoken”grammatical” error detection and correction, both of which provide important features that can be used in downstream feedback systems One approach to addressing this lack of data is to convert the output of an automatic speech recognition (ASR) system into a form that is closer to text data, for which there is significantly more labelled data available. Disfluency detection, locating regions of the speech where for example false starts and repetitions occur, and subsequent removal of the associated words, helps to make speech transcriptions more text-like. Additionally, ASR systems do not usually generate sentence-like units, the output is simply a sequence of words associated with the particular speech segmentation used for coding. This motivates the need for automated systems for sentence segmentation. By combining these approaches, advanced text processing techniques should perform significantly better on the output from spoken language processing systems. Unfortunately there is not enough labelled data available to train these systems on spoken learner English. In this work disfluency detection and”sentence” segmentation systems trained on data from native speakers are applied to spoken grammatical error detection and correction tasks for learners of English. Performance gains using these approaches are shown on a free speaking test.
Cambridge Assessment (Unknown)