Quantification of Transducer Misalignment in Ultrasound Tongue Imaging
This addresses a specific, incremental issue in speech production research for improving data quality in ultrasound imaging.
The paper tackled the problem of transducer misalignment in ultrasound tongue imaging during speech recordings by proposing a quantification approach using MSE and similarity metrics, with results showing that large MSE and small SSIM values indicate issues like misalignment or lack of gel in datasets of Hungarian and Scottish English children.
In speech production research, different imaging modalities have been employed to obtain accurate information about the movement and shaping of the vocal tract. Ultrasound is an affordable and non-invasive imaging modality with relatively high temporal and spatial resolution to study the dynamic behavior of tongue during speech production. However, a long-standing problem for ultrasound tongue imaging is the transducer misalignment during longer data recording sessions. In this paper, we propose a simple, yet effective, misalignment quantification approach. The analysis employs MSE distance and two similarity measurement metrics to identify the relative displacement between the chin and the transducer. We visualize these measures as a function of the timestamp of the utterances. Extensive experiments are conducted on a Hungarian and Scottish English child dataset. The results suggest that large values of Mean Square Error (MSE) and small values of Structural Similarity Index (SSIM) and Complex Wavelet SSIM indicate corruptions or issues during the data recordings, which can either be caused by transducer misalignment or lack of gel.