  Multi-task learning (MTL) frameworks have proven to be effective in diverse
speech related tasks like automatic speech recognition (ASR) and speech emotion
recognition. This paper proposes a MTL framework to perform
acoustic-to-articulatory speech inversion by simultaneously learning an
acoustic to phoneme mapping as a shared task. We use the Haskins Production
Rate Comparison (HPRC) database which has both the electromagnetic
articulography (EMA) data and the corresponding phonetic transcriptions.
Performance of the system was measured by computing the correlation between
estimated and actual tract variables (TVs) from the acoustic to articulatory
speech inversion task. The proposed MTL based Bidirectional Gated Recurrent
Neural Network (RNN) model learns to map the input acoustic features to nine
TVs while outperforming the baseline model trained to perform only acoustic to
articulatory inversion.
