Voice conversion through vector quantization

Abe, Masanobu; Nakamura, Satoshi; Shikano, Kiyohiro; Kuwabara, Hisao

doi:10.1109/icassp.1988.196671

Cited by 303 publications

(85 citation statements)

References 3 publications

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“…al. [1]. Researchers have tried to transform only the filter features [2] to get an acceptable quality of voice transformation.…”

Section: Introductionmentioning

confidence: 99%

“…But the work presented by [3] proved the need for transformation of excitation features to attain an effective voice morphing system. A variety of techniques have been proposed by researchers for the conversion function, such as mapping code books [1], artificial neural networks [4] [5], dynamic frequency warping [2] or Gaussian mixture model [3] [6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Voice conversion using Artificial Neural Networks

Desai

Raghavendra

Yegnanarayana

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

176

116

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In this paper, we propose to use Artificial Neural Networks (ANN) for voice conversion. We have exploited the mapping abilities of ANN to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using ANN and the state-of-the-art Gaussian Mixture Model (GMM) is conducted. The results of voice conversion evaluated using subjective and objective measures confirm that ANNs perform better transformation than GMMs and the quality of the transformed speech is intelligible and has the characteristics of the target speaker.Index Terms-Voice conversion, Artificial Neural Networks, Gaussian Mixture Model.

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“…al. [1]. Researchers have tried to transform only the filter features [2] to get an acceptable quality of voice transformation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Voice conversion using Artificial Neural Networks

Desai

Raghavendra

Yegnanarayana

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

176

116

View full text Add to dashboard Cite

show abstract

“…In text-dependent methods, training procedures are based on parallel corpora, i.e., training data have the source and the target speakers uttering the same text. Such methods include vector quantization [2,7], linear transformation [38,84], formant transformation [77], vocal tract length normalization (VTLN) [71], and prosodic transformation [7]. In text-independent voice conversion techniques, the system is trained with source and target speakers uttering different texts.…”

Section: Speech Processingmentioning

confidence: 99%

“…Speaker transformation techniques [28,39,85,40,2,7,77,62,16] might involve modifications of different aspects of the speech signal that carries the speaker's identity. We can cite different methods.…”

Section: Speech Processingmentioning

confidence: 99%

Multimodal Human Machine Interactions in Virtual and Augmented Reality

Chollet

Esposito

Gentès

et al. 2009

Multimodal Signals: Cognitive and Algorithmic Issues

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Abstract. Virtual worlds are developing rapidly over the Internet. They are visited by avatars and staffed with Embodied Conversational Agents (ECAs). An avatar is a representation of a physical person. Each person controls one or several avatars and usually receives feedback from the virtual world on an audio-visual display. Ideally, all senses should be used to feel fully embedded in a virtual world. Sound, vision and sometimes touch are the available modalities. This paper reviews the technological developments which enable audio-visual interactions in virtual and augmented reality worlds. Emphasis is placed on speech and gesture interfaces, including talking face analysis and synthesis.

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“…The speech fundamental frequency (F0) [1] is an important speech parameter which has significant effect in domains such as voiceprint analysis [2], speaker recognition [3], voice conversion [4], [5] and etc. Now lots of speech analysis/synthesis [6], [7] frameworks use fundamental frequency as an important model parameter such as STRAIGHT [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

A Precision, Large Scale, Anti-Noise Method for Correction of Speech Fundamental Frequency

Zhao¹,

Chen²,

Yun³

et al. 2014

JACN

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Abstract-The speech fundamental frequency (F0) is an important speech parameter which has significant effect in domains such as voiceprint analysis, speaker recognition, voice conversion and etc. The existing extraction methods of speech fundamental frequency are not satisfactory in terms of accuracy due to the limitations of the methods themselves and noise so that correction is essential for high-quality analysis, recognition, conversion. The effect of existing correction methods rely heavily on the accuracy of first extraction, and are sensitive to noise. This article proposes a new correction method which makes use of cross-correlation of original signal and estimated signal to convert the difference between their frequencies to phase difference, thereby gains the accurate fundamental frequency by measuring this phase difference. Experiments indicate that the proposed method is effective when the estimated frequency ranges from 70% to 190% of the actual frequency, and keeps the average error within -50dB (0.35%) when signal to noise ratio (SNR) reduces to 3dB.Index Terms-Speech fundamental frequency, precision correction, large scale, anti-noise, phase difference.

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Voice conversion through vector quantization

Cited by 303 publications

References 3 publications

Voice conversion using Artificial Neural Networks

Voice conversion using Artificial Neural Networks

Multimodal Human Machine Interactions in Virtual and Augmented Reality

A Precision, Large Scale, Anti-Noise Method for Correction of Speech Fundamental Frequency

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