Discovering phoneme-specific critical articulators through a data-driven approach

We propose an approach for learning critical articulators for phonemes through a machine learning approach. We formulate the learning with three models trained end to end. First, we use Acoustic to Articulatory Inversion (AAI) to predict time-varying speech articulators EMA. We also predict the phoneme-specific weights across articulators for each frame. To avoid overfitting, we also add a dropout layer before the weights prediction layer. Next, we normalize the predicted weights across articulators using min-max normalization for each frame. The normalized weights are multiplied by the ground truth $EMA$ and then we try to predict the phones at each frame. We train this whole setup end to end and use two losses. One loss is for the phone prediction which is the cross entropy loss and the other is for the AAI prediction which is the mean squared error loss. To maintain gradient flow between the phone prediction block and the $EMA$ prediction block, we use straight-through estimation. The goal here is to predict the weights of the articulator at each frame while training the model end to end.

Speaking rate attention-based duration prediction for speed control TTS

With the advent of high-quality speech synthesis, there is a lot of interest in controlling various prosodic attributes of speech. Speaking rate is an essential attribute towards modelling the expressivity of speech. In this work, we propose a novel approach to control the speaking rate for non-autoregressive TTS. We achieve this by conditioning the speaking rate inside the duration predictor, allowing implicit speaking rate control. We show the benefits of this approach by synthesising audio at various speaking rate factors and measuring the quality of speaking rate-controlled synthesised speech. Further, we study the effect of the speaking rate distribution of the training data towards effective rate control. Finally, we fine-tune a baseline pretrained TTS model to obtain speaking rate control TTS. We provide various analyses to showcase the benefits of using this proposed approach, along with objective as well as subjective metrics. We find that the proposed methods have higher subjective scores and lower speaker rate errors across many speaking rate factors over the baseline.