Silent Speech and Emotion Recognition from Vocal Tract Shape Dynamics in Real-Time MRI
This enables speech recognition from articulatory motions without audio, potentially aiding individuals with speech impairments, though it is incremental as it builds on existing MRI and deep learning methods.
The authors tackled silent speech recognition by translating real-time MRI videos of vocal tract shapes into text, achieving a 40.6% phoneme error rate on the USC-TIMIT corpus, and analyzed how vocal tract sub-regions vary with emotion and gender.
Speech sounds of spoken language are obtained by varying configuration of the articulators surrounding the vocal tract. They contain abundant information that can be utilized to better understand the underlying mechanism of human speech production. We propose a novel deep neural network-based learning framework that understands acoustic information in the variable-length sequence of vocal tract shaping during speech production, captured by real-time magnetic resonance imaging (rtMRI), and translate it into text. The proposed framework comprises of spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end. On the USC-TIMIT corpus, the model achieved a 40.6% PER at sentence-level, much better compared to the existing models. To the best of our knowledge, this is the first study that demonstrates the recognition of entire spoken sentence based on an individual's articulatory motions captured by rtMRI video. We also performed an analysis of variations in the geometry of articulation in each sub-regions of the vocal tract (i.e., pharyngeal, velar and dorsal, hard palate, labial constriction region) with respect to different emotions and genders. Results suggest that each sub-regions distortion is affected by both emotion and gender.