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Journal ArticleDOI

Voice transformation using PSOLA technique

H. Valbret, +2 more
- Vol. 11, Iss: 2, pp 175-187
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TLDR
A new system for voice conversion is described that combines a PSOLA (Pitch Synchronous Overlap and Add)-derived synthesizer and a module for spectral transformation, which produces a satisfyingly natural “transformed” voice.
Abstract
In this contribution, a new system for voice conversion is described. The proposed architecture combines a PSOLA (Pitch Synchronous Overlap and Add)-derived synthesizer and a module for spectral transformation. The synthesizer based on the classical source-filter decomposition allows prosodic and spectral transformations to be performed independently. Prosodic modifications are applied on the excitation signal using the TD-PSOLA scheme; converted speech is then synthesized using the transformed spectral parameters. Two different approaches to derive spectral transformations, borrowed from the speech-recognition domain, are compared: Linear Multivariate Regression (LMR) and Dynamic Frequency Warping (DFW). Vector-quantization is carried out as a preliminary stage to render the spectral transformations dependent of the acoustical realization of sounds. A formal listening test shows that the synthesizer produces a satisfyingly natural “transformed” voice. LMR proves yet to allow a slightly better conversion than DFW. Still there is room for improvement in the spectral transformation stage.

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Citations
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Journal ArticleDOI

Voice Conversion Using Dynamic Frequency Warping With Amplitude Scaling, for Parallel or Nonparallel Corpora

TL;DR: The proposed DFW with Amplitude scaling (DFWA) outperforms standard GMM and hybrid GMM-DFW methods for VC in terms of both speech quality and timbre conversion, as is confirmed in extensive objective and subjective testing.
MonographDOI

Applied Speech and Audio Processing: With Matlab Examples

TL;DR: This practically oriented text provides MATLAB examples throughout to illustrate the concepts discussed and to give the reader hands-on experience with important techniques, and is ideal for graduate students and practitioners working with speech or audio systems.
Patent

Method and system for non-parametric voice conversion

TL;DR: In this paper, a text-to-speech (TTS) synthesis system may include hidden Markov model (HMM) HMM based speech modeling for both synthesizing output speech.
Journal ArticleDOI

The SIGMA Algorithm: A Glottal Activity Detector for Electroglottographic Signals

TL;DR: This paper describes a new method for EGG-based glottal activity detection which exhibits high accuracy over the entirety of voiced segments, including, in particular, the transition regions, thereby giving significant improvement over existing methods.
References
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Journal ArticleDOI

An Algorithm for Vector Quantizer Design

TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Book

Linear Prediction of Speech

John E. Markel, +1 more
TL;DR: Speech Analysis and Synthesis Models: Basic Physical Principles, Speech Synthesis Structures, and Considerations in Choice of Analysis.
Journal ArticleDOI

Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones

TL;DR: In a common framework several algorithms that have been proposed recently, in order to improve the voice quality of a text-to-speech synthesis based on acoustical units concatenation based on pitch-synchronous overlap-add approach are reviewed.
Proceedings ArticleDOI

Voice conversion through vector quantization

TL;DR: The authors propose a new voice conversion technique through vector quantization and spectrum mapping which makes it possible to precisely control voice individuality.
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