Automatic recognition of children's read speech for stuttering application

Abstract

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 automatically 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.

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

“…Examples in the paper demonstrate how this approach can be used to ignore some partial-word repetitions and blocks. Alharbi et al [2,3] focused on stuttered speech from kids that incorporates the structure of repetitions and other dyfluencies into an augmented language model that is better at including dysfluencies in a transcription. In our work, we focus on solutions, like Mitra et al's [51], which can be applied on top of existing recognition systems and do not require as much data as end-to-end solutions.…”

Sequence labeling to detect stuttering events in read speech

“…We previously described this ASR system in [45]; but the focus of the current work is on processing the transcriptions generated by it.…”

SEP-28k: A Dataset for Stuttering Event Detection from Podcasts with People Who Stutter

“…The focus of this paper is on detection of five stuttering event types: Blocks, Prolongations, Sound Repetitions, Word/Phrase Repetitions, and Interjections. Existing work has explored this problem using traditional signal processing techniques [15,16,17], language modeling (LM) [12,18,19,20,21], and acoustic modeling (AM) [21,10]. Each approach has be shown to be effective 1.…”