Silent Speech and Emotion Recognition from Vocal Tract Shape Dynamics in Real-Time MRI

TL;DR

This paper introduces a novel deep learning framework that recognizes spoken sentences and emotions from real-time MRI of vocal tract movements, achieving significant improvements over previous models.

Contribution

It is the first to demonstrate sentence recognition from rtMRI data and analyzes how vocal tract geometry varies with emotion and gender.

Findings

Achieved 40.6% PER on USC-TIMIT corpus.

First to recognize entire sentences from rtMRI data.

Vocal tract sub-region distortions vary with emotion and gender.

Abstract

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.