DeepAI AI Chat
Log In Sign Up

Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels

by   Branislav Gerazov, et al.

The way infants use auditory cues to learn to speak despite the acoustic mismatch of their vocal apparatus is a hot topic of scientific debate. The simulation of early vocal learning using articulatory speech synthesis offers a way towards gaining a deeper understanding of this process. One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target. We contribute with evaluating the performance of a set of 40 feature-metric combinations for the task of optimising the production of static vowels with a high-quality articulatory synthesiser. Towards this end we assess the usability of formant error and the projection of the feature-metric error surface in the normalised F1-F2 formant space. We show that this approach can be used to evaluate the impact of features and metrics and also to offer insight to perceptual results.


P-Reverb: Perceptual Characterization of Early and Late Reflections for Auditory Displays

We introduce a novel, perceptually derived metric (P-Reverb) that relate...

On incorporating social speaker characteristics in synthetic speech

In our previous work, we derived the acoustic features, that contribute ...

Using multimodal speech production data to evaluate articulatory animation for audiovisual speech synthesis

The importance of modeling speech articulation for high-quality audiovis...

Evaluating Subtitle Segmentation for End-to-end Generation Systems

Subtitles appear on screen as short pieces of text, segmented based on f...

Beelines: Evaluating Motion Prediction Impact on Self-Driving Safety and Comfort

The commonly used metrics for motion prediction do not correlate well wi...

A Psychoacoustic Quality Criterion for Path-Traced Sound Propagation

In developing virtual acoustic environments, it is important to understa...