Journal ArticleDOI
Voice transformation using PSOLA technique
H. Valbret,Eric Moulines,J. P. Tubach +2 more
- Vol. 11, Iss: 2, pp 175-187
Reads0
Chats0
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.read more
Citations
More filters
Book ChapterDOI
Using Mandarin Training Corpus to Realize a Mandarin-Tibetan Cross-Lingual Emotional Speech Synthesis
Peiwen Wu,Hongwu Yang,Zhenye Gan +2 more
TL;DR: Subjective evaluations and objective tests show that the method can synthesize both emotional Mandarin speech and emotional Tibetan speech with high naturalness and emotional similarity and can be adopted to realizing an emotional speech synthesis with exiting emotional training corpus for languages lacking emotional speech resources.
Journal ArticleDOI
Automatic recognition of gemination in Japanese motivated by perceptual experiments
Journal ArticleDOI
Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition
TL;DR: An innovative parallel dictionary-learning method using non-negative Tucker decomposition (NTD) is proposed, which estimates the dictionary matrix for NMF-VC without using parallel data.
Improving of Segmental LMR-Mapping Based Voice Conversion Method.
Hung-Yan Gu,Jia-Wei Chang +1 more
TL;DR: According to the measured VR (variance ratio) values and the scores of subjective listening tests, the quality of the converted voice will become better when HEQ is added, and become much better when TFS is added.
Voice Conversion based on Non-negative Matrix Factorization with Segment Features in Noisy Environments
TL;DR: Voice Conversion based on Non-negative Matrix Factorization with Segment Features in Noisy Environments with Se segment features in noisy En environments.
References
More filters
Journal ArticleDOI
An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
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,A. Gray +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.