  Ultrasound tongue imaging (UTI) provides a convenient way to visualize the
vocal tract during speech production. UTI is increasingly being used for speech
therapy, making it important to develop automatic methods to assist various
time-consuming manual tasks currently performed by speech therapists. A key
challenge is to generalize the automatic processing of ultrasound tongue images
to previously unseen speakers. In this work, we investigate the classification
of phonetic segments (tongue shapes) from raw ultrasound recordings under
several training scenarios: speaker-dependent, multi-speaker,
speaker-independent, and speaker-adapted. We observe that models underperform
when applied to data from speakers not seen at training time. However, when
provided with minimal additional speaker information, such as the mean
ultrasound frame, the models generalize better to unseen speakers.
