DocumentCode
806675
Title
Transforming Perceived Vocal Effort and Breathiness Using Adaptive Pre-Emphasis Linear Prediction
Author
Nordstrom, Karl I. ; Tzanetakis, George ; Driessen, Peter F.
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC
Volume
16
Issue
6
fYear
2008
Firstpage
1087
Lastpage
1096
Abstract
This paper presents a technique to transform high-effort voices into breathy voices using adaptive pre-emphasis linear prediction (APLP). The primary benefit of this technique is that it estimates a spectral emphasis filter that can be used to manipulate the perceived vocal effort. The other benefit of APLP is that it estimates a formant filter that is more consistent across varying voice qualities. This paper describes how constant pre-emphasis linear prediction (LP) estimates a voice source with a constant spectral envelope even though the spectral envelope of the true voice source varies over time. A listening experiment demonstrates how differences in vocal effort and breathiness are audible in the formant filter estimated by constant pre-emphasis LP. APLP is presented as a technique to estimate a spectral emphasis filter that captures the combined influence of the glottal source and the vocal tract upon the spectral envelope of the voice. A final listening experiment demonstrates how APLP can be used to effectively transform high-effort voices into breathy voices. The techniques presented here are relevant to researchers in voice conversion, voice quality, singing, and emotion.
Keywords
estimation theory; filtering theory; speech processing; adaptive pre-emphasis linear prediction; high-effort voice transform; spectral emphasis filter; spectral envelope; voice conversion; voice quality; Acoustic noise; Computer science; Filters; Natural languages; Noise reduction; Predictive models; Speech processing; Terminology; Adaptive pre-emphasis; breathiness; linear prediction (LP); pre-emphasis; spectral slope; vocal effort; voice quality;
fLanguage
English
Journal_Title
Audio, Speech, and Language Processing, IEEE Transactions on
Publisher
ieee
ISSN
1558-7916
Type
jour
DOI
10.1109/TASL.2008.2001105
Filename
4566083
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