An Investigation of Muscle Models for Physiologically Based Intonation Modelling

Prosody is a crucial aspect of the speech signal and its modelling is of great importance for various speech technologies. Intonation models based on physiology rely on an accurate model of muscle activation. Although most of them are based on the spring-damper-mass (SDM) muscle model, the more complex Hill type model offers a more accurate representation of muscle dynamics. In this paper we analyse and compare these two muscle models and discuss the benefits and disadvantages they bring. This research is a part of an on-going effort to develop an improved intonation model

Artificial Vocal Learning guided by Phoneme Recognition and Visual Information

This paper introduces a paradigm shift regarding vocal learning simulations, in which the communicative function of speech acquisition determines the learning process and intelligibility is considered the primary measure of learning success. Thereby, a novel approach for artificial vocal learning is presented that utilizes deep neural network-based phoneme recognition in order to calculate the speech acquisition objective function. This function guides a learning framework that involves the state-of-the-art articulatory speech synthesizer VocalTractLab as the motor-to-acoustic forward model. In this way, an extensive set of German phonemes, including most of the consonants and all stressed vowels, was produced successfully. The synthetic phonemes were rated as highly intelligible by human listeners. Furthermore, it is shown that visual speech information, such as lip and jaw movements, can be extracted from video recordings and be incorporated into the learning framework as an additional loss component during the optimization process. It was observed that this visual loss did not increase the overall intelligibility of phonemes. Instead, the visual loss acted as a regularization mechanism that facilitated the finding of more biologically plausible solutions in the articulatory domain

Intonation modelling using a muscle model and perceptually weighted matching pursuit

We propose a physiologically based intonation model using perceptual relevance. Motivated by speech synthesis from a speech-to-speech translation (S2ST) point of view, we aim at a language independent way of modelling intonation. The model presented in this paper can be seen as a generalisation of the command response (CR) model, albeit with the same modelling power. It is an additive model which decomposes intonation contours into a sum of critically damped system impulse responses. To decompose the intonation contour, we use a weighted correlation based atom decomposition algorithm (WCAD) built around a matching pursuit framework. The algorithm allows for an arbitrary precision to be reached using an iterative procedure that adds more elementary atoms to the model. Experiments are presented demonstrating that this generalised CR (GCR) model is able to model intonation as would be expected. Experiments also show that the model produces a similar number of parameters or elements as the CR model. We conclude that the GCR model is appropriate as an engineering solution for modelling prosody, and hope that it is a contribution to a deeper scientific understanding of the neurobiological process of intonation

Self-Supervised Solution to the Control Problem of Articulatory Synthesis

Given an articulatory-to-acoustic forward model, it is a priori unknown how its motor control must be operated to achieve a desired acoustic result. This control problem is a fundamental issue of articulatory speech synthesis and the cradle of acousticto-articulatory inversion, a discipline which attempts to address the issue by the means of various methods. This work presents an end-to-end solution to the articulatory control problem, in which synthetic motor trajectories of Monte-Carlo-generated artificial speech are linked to input modalities (such as natural speech recordings or phoneme sequence input) via speakerindependent latent representations of a vector-quantized variational autoencoder. The proposed method is self-supervised and thus, in principle, synthesizer and speaker model independent

Exploration strategies for articulatory synthesis of complex syllable onsets

High-quality articulatory speech synthesis has many potential applications in speech science and technology. However, developing appropriate mappings from linguistic specification to articulatory gestures is difficult and time consuming. In this paper we construct an optimisation-based framework as a first step towards learning these mappings without manual intervention. We demonstrate the production of CCV syllables and discuss the quality of the articulatory gestures with reference to coarticulation

Modelling English diphthongs with dynamic articulatory targets

The nature of English diphthongs has been much disputed. By now, the most influential account argues that diphthongs are phoneme entities rather than vowel combinations. However, mixed results have been reported regarding whether the rate of formant transition is the most reliable attribute in the perception and production of diphthongs. Here, we used computational modelling to explore the underlying forms of diphthongs. We tested the assumption that diphthongs have dynamic articulatory targets by training an articulatory synthesiser with a three-dimensional (3D) vocal tract model to learn English words. An automatic phoneme recogniser was constructed to guide the learning of the diphthongs. Listening experiments by native listeners indicated that the model succeeded in learning highly intelligible diphthongs, providing support for the dynamic target assumption. The modelling approach paves a new way for validating hypotheses of speech perception and production