Ultrasound tongue imaging for diarization and alignment of child speech therapy sessions
This work addresses the challenge of enhancing speech therapy analysis for children, though it appears incremental as it builds on existing methods by adding ultrasound data.
The paper tackled the problem of automatically processing child speech therapy sessions by using ultrasound tongue imaging to improve speaker diarization and word-alignment tasks, resulting in systems augmented with ultrasound data outperforming audio-only systems.
We investigate the automatic processing of child speech therapy sessions using ultrasound visual biofeedback, with a specific focus on complementing acoustic features with ultrasound images of the tongue for the tasks of speaker diarization and time-alignment of target words. For speaker diarization, we propose an ultrasound-based time-domain signal which we call estimated tongue activity. For word-alignment, we augment an acoustic model with low-dimensional representations of ultrasound images of the tongue, learned by a convolutional neural network. We conduct our experiments using the Ultrasuite repository of ultrasound and speech recordings for child speech therapy sessions. For both tasks, we observe that systems augmented with ultrasound data outperform corresponding systems using only the audio signal.