Stuttering is a common speech disfluency that may persist into adulthood if not treated in its early stages. Techniques from spoken language understanding may be applied to provide automated diagnoses of stuttering from voice recordings; however, there are several difficulties, including the lack of training data involving young children and the high dimensionality of these data. This study investigates how automatic speech recognition (ASR) could help clinicians by providing a tool that

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... ically recognises stuttering events and provides a useful written transcription of what was said. In addition, to enhance the performance of ASR and to alleviate the lack of stuttering data, this study examines the effect of augmenting the language model with artificially generated data. The performance of the ASR tool with and without language model augmentation is compared. Following language model augmentation, the ASR tool's performance improved recall from 38% to 62.2% and precision from 56.58% to 71%. When mis-recognised events are more coarsely classified as stuttering/ non-stuttering events, the performance improves up to 73% in recall and 84% in precision. Although the obtained results are not perfect, they map to fairly robust stutter/ non-stutter decision boundaries.