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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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Proceedings ArticleDOI

Using phone and diphone based acoustic models for voice conversion: a step towards creating voice fonts

TL;DR: It is shown that by using the proposed approach, voice fonts can be created and stored which represent individual characteristics of a particular speaker, to be used for customization of synthetic speech.
Proceedings ArticleDOI

A new voice transformation method based on both linear and nonlinear prediction analysis

TL;DR: A listening test shows that the proposed voice transformation method makes it possible to convert speaker's individuality while maintaining high quality.
Journal ArticleDOI

A Log Domain Pulse Model for Parametric Speech Synthesis

TL;DR: A new signal model is proposed that leads to a simple synthesizer, without the need for ad-hoc tuning of model parameters, which adopts a combination of speech components that are additive in the log domain.
Proceedings ArticleDOI

Sparse representation for frequency warping based voice conversion

TL;DR: This paper presents a sparse representation framework for weighted frequency warping based voice conversion that not only avoids the statistical averaging caused by GMM but also preserves the high-resolution spectral details for high-quality converted speech.
Proceedings ArticleDOI

Pitch transposition and breathiness modification using a glottal source model and its adapted vocal-tract filter

TL;DR: This paper addresses the pitch transposition and the modification of breathiness by means of an analytic description of the deterministic component of the voice source, a glottal model, and it is shown that the breathiness of two male utterances can be controlled.
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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