While acoustic analysis methods have become a commodity in voice emotion research,
experiments that attempt not only to describe but to computationally manipulate expressive …

Feature-based vocoders, eg, STRAIGHT, offer a way to manipulate the perceived
characteristics of the speech signal in speech transformation and synthesis. For the …

Supervised deep learning approaches to underdetermined audio source separation achieve
state-of-the-art performance but require a dataset of mixtures along with their corresponding …

Voice models often use frequency limits to split the speech spectrum into two or more
voiced/unvoiced frequency bands. However, from the voice production, the amplitude …

The use of the mel spectrogram as a signal parameterization for voice generation is quite
recent and linked to the development of neural vocoders. These are deep neural networks …

In this publication, we present a deep learning-based method to transform the f 0 in speech
and singing voice recordings. f 0 transformation is performed by training an auto-encoder on …

The estimation of glottal flow from a speech waveform is an essential technique used in
speech analysis and parameterisation. Significant research effort has been addressed at …

This paper proposes an analysis method to separate the glottal source and vocal tract
components of speech that is called Glottal Spectral Separation (GSS). This method can …

Most of the degradation in current Statistical Parametric Speech Synthesis (SPSS) results
from the form of the vocoder. One of the main causes of degradation is the reconstruction of …

Whisper-to-speech conversion is motivated by laryngeal disorders, in which malfunction of
the vocal folds leads to loss of voicing. Many patients with laryngeal disorders can still …