Generation of growl-type voice qualities by spectral morphing

Abstract

In this paper we introduce a morph-based approach for generating voice source aperiodicities frequently associated with strong vocal expressions, especially in singing. In our approach the excitation characteristics of one signal are combined the fundamental frequency and spectral envelope characteristics of another signal. An exemplar sustained sample of the target voice quality is looped and resampled in the time domain in order to generate a continuous signal matching the input voice's fundamental frequency. This operation preserves most of the sample's voice quality characteristics at the cost of scaling time and frequency dimensions. While we found the temporal scaling to be acceptable in many contexts, the frequency scaling has to be inverted in order to generate appropriate spectral content for the source excitation's entire bandwidth. This can be accomplished by the phase-locked vocoder method of modulating harmonic bands in frequency. Finally, the input signal's harmonic amplitudes and phases are applied to the transformed morph sample, allowing for a simple one-dimensional control of morph amount by linear interpolation with the input signal. The proposed system is evaluated and the results are discussed.

Beyond Correlation: Acoustic Transformation Methods for the Experimental Study of Emotional Voice and Speech

“…In theory, analysis-resynthesis techniques can be used to, first, estimate the recording’s series of glottal pulses (Degottex, Lanchantin, Roebel, & Rodet, 2013) and then, resynthesize the vocal signal from a manipulated series of pulses with artificially varied amplitude and period (Bõhm, Audibert, Shattuck-Hufnagel, Németh, & Aubergé, 2008; Ruinskiy & Lavner, 2008; Verma & Kumar, 2005). However, these techniques rely on an explicit model of pulse variability, which is typically learned from one or several target examples of naturally rough voices (Bonada & Blaauw, 2013), and it is unclear how such predetermined patterns should be selected for arbitrary voices. Moreover, because of the computational complexity of the initial stage of glottal source estimation, these techniques cannot operate in real time.…”

Expressive Singing Synthesis Based on Unit Selection for the Singing Synthesis Challenge 2016

“…Another motivation is the participation in the Singing Synthesis Challenge 2016. In particular, this work is a continuation of our previous contributions on the expression control of singing voice synthesis [6,7] and on voice modeling [8,9].…”

ANGUS: Real-time manipulation of vocal roughness for emotional speech transformations

“…Adding vocal non-linearities on such signals can be done with analysis-resynthesis techniques that, first, estimate the recording's series of glottal pulses [20] and, then, resynthesize the vocal signal from a manipulated series of pulses with artificially-varied amplitude and period [21][22][23][24]. However, these techniques rely on an explicit model of pulse variability, which is typically learned from one or several target examples of naturally rough voices [25], and it is unclear how such predetermined patterns should be selected for unknown voices. Moreover, because of the computational complexity of the initial stage of glottal source estimation, these techniques cannot operate in real-time, i.e.…”