Multimodal Representations for Synchronized Speech and Real-Time MRI Video Processing

Representations for data subunits can help with recent data accumulation by enabling efficient storage and retrieval systems. In this paper, we investigate the problem of representation generation for phone classification and cross-modal same-different word discrimination tasks. The benefits of utilizing multimodal data on these tasks are examined together with different data fusion schemes. Mainly, the paper considers two different data modalities, upper airway mid-sagittal plane real-time magnetic resonance imaging (rtMRI) videos and the corresponding speech waveforms, and experiments on USC-TIMIT rtMRI dataset. For the phone classification task, two unimodal neural networks are designed, and these separate systems are merged in two different ways that provide data fusion between two modalities. The proposed networks differ in their stages in which they perform the data fusion. As hypothesized, our results show that data fusion indeed brings a performance improvement over both unimodal approaches, and performing fusion in earlier stages with cross-connections yields better results than fusing the data in later stages. In addition to the proposed phone classification schemes, different unimodal and multimodal systems are designed to obtain phone recognition results on USC-TIMIT rtMRI dataset. Phone representations generated for the phone classification task are also utilized in the phone recognition task, and their representative power is illustrated. Finally, we define a cross-view same-different word discrimination task on USC-TIMIT. We propose two different schemes to tackle this task, and find that for cross-view same-different discrimination, generating representations with the help of cross-modality yields better accuracy than a system employing independently created representations.