Ultrasound tongue imaging for diarization and alignmentof child speech therapy sessions
Table 1: Results for speaker diarization for typically developing speech (UXTD) and disordered speech (UXSSD) . Precision, recall, and F1 score are measured in terms of retrieved child speech. Diarization Error Rate (DER) measures silence and speech, while Confusion Error (Conf) measures child and therapist speech error. Highlighted results indicate best systems.
['[BOLD] Systems', '[BOLD] Prec', '[BOLD] Rec', '[BOLD] F1', '[BOLD] DER', '[BOLD] Conf']
The simple baseline approaches perform well for the typically developing dataset, which has minimal adult intervention, and therefore less need for diarization [eshky2018ultrasuite]. The loss in recall with ETA thresholding is caused by losing segments with reduced tongue activity (e.g. words such as “apa”). In the case of the disordered speech dataset, therapist intervention is more frequent, and ETA is a useful addition to VAD. The i-vector system does not perform as well as expected. This might be due to size of the database used to train the UBM, but also to the challenges of fast speaker turns. Similar results were found using a large adult database (NIST SRE datasets, similar to [sell2014speaker]). We hypothesize that the i-vectors are not robust enough, as we enforce a very small window due to the rapid turns between speakers. High error rates have previously been found for a similar scenario [cristia2018talker]. Allowing a longer window would lead to equally poor i-vectors, as they would be computed over a mixture of adult and child speech. Surprisingly, including f0 was not helpful using this method.