Noise-Robust Voice Conversion Based on Sparse Spectral Mapping Using Non-negative Matrix Factorization

Aihara, Ryo; Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

doi:10.1587/transinf.e97.d.1411

Cited by 13 publications

(8 citation statements)

References 19 publications

(27 reference statements)

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“…In order to retain the quality of converted voices in a noisy environment, noise robustness is required. In our previous study [46], a noise-robust NMF-based VC was proposed, where the performance was improved by 25% compared with the GMM-based method. As the currently proposed method is based on NMF-based VC, it will be easy to apply the noise-robust conversion.…”

Section: Resultsmentioning

confidence: 99%

Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition

Takashima

Nakashika

Takiguchi

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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Voice conversion (VC) is a technique of exclusively converting speaker-specific information in the source speech while preserving the associated phonemic information. Non-negative matrix factorization (NMF)-based VC has been widely researched because of the natural-sounding voice it achieves when compared with conventional Gaussian mixture model-based VC. In conventional NMF-VC, models are trained using parallel data which results in the speech data requiring elaborate pre-processing to generate parallel data. NMF-VC also tends to be an extensive model as this method has several parallel exemplars for the dictionary matrix, leading to a high computational cost. In this study, an innovative parallel dictionary-learning method using non-negative Tucker decomposition (NTD) is proposed. The proposed method uses tensor decomposition and decomposes an input observation into a set of mode matrices and one core tensor. The proposed NTD-based dictionary-learning method estimates the dictionary matrix for NMF-VC without using parallel data. The experimental results show that the proposed method outperforms other methods in both parallel and non-parallel settings.

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Section: Resultsmentioning

confidence: 99%

Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition

Takashima

Nakashika

Takiguchi

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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“…Wu et al proposed a method for NMF-based VC to reduce the computational cost [35]. In [4], we also proposed a frame-work that reduces computational time for NMF-based VC. We will combine these methods and investigate the optimal number of bases for better performance.…”

Section: Resultsmentioning

confidence: 99%

“…Wu et al [35] applied a spectrum compression factor to NMF-based VC to improve conversion quality. Second, our NMF-based VC method is noise robust [4]. The noise exemplars, which are extracted from the before-and after-utterance sections in the observed signal, are used as the noise dictionary, and the VC process is combined with NMF-based noise reduction.…”

Section: Related Workmentioning

confidence: 99%

Parallel Dictionary Learning for Multimodal Voice Conversion Using Matrix Factorization

Aihara

Masaka

Takiguchi

et al. 2016

Computer and Information Science

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Parallel dictionary learning for multimodal voice conversion is proposed in this paper. Because of noise robustness of visual features, multimodal feature has been attracted in the field of speech processing, and we have proposed multimodal VC using Non-negative Matrix Factorization (NMF). Experimental results showed that our conventional multimodal VC can effectively converted in a noisy environment, however, the difference of conversion quality between audio input VC and multimodal VC is not so large in a clean environment. We assume this is because our exemplar dictionary is over-complete. Moreover, because of non-negativity constraint for visual features, our conventional multimodal NMF-based VC cannot factorize visual features effectively. In order to enhance the conversion quality of our NMF-based multimodal VC, we propose parallel dictionary learning. Non-negative constraint for visual features is removed so that we can handle visual features which include negative values. Experimental results showed that our proposed method effectively converted multimodal features in a clean environment.

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“…The proposed method requires higher computation times than the GMM-based method. In [25], we proposed a framework that reduces computational time for NMF-based VC. In future work, we will investigate the optimal number of bases and evaluate performance under other noise conditions.…”

Section: Discussionmentioning

confidence: 99%

Small-parallel exemplar-based voice conversion in noisy environments using affine non-negative matrix factorization

Aihara

Fujii

Nakashika

et al. 2015

J AUDIO SPEECH MUSIC PROC.

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The need to have a large amount of parallel data is a large hurdle for the practical use of voice conversion (VC). This paper presents a novel framework of exemplar-based VC that only requires a small number of parallel exemplars. In our previous work, a VC technique using non-negative matrix factorization (NMF) for noisy environments was proposed. This method requires parallel exemplars (which consist of the source exemplars and target exemplars that have the same texts uttered by the source and target speakers) for dictionary construction. In the framework of conventional Gaussian mixture model (GMM)-based VC, some approaches that do not need parallel exemplars have been proposed. However, in the framework of exemplar-based VC for noisy environments, such a method has never been proposed. In this paper, an adaptation matrix in an NMF framework is introduced to adapt the source dictionary to the target dictionary. This adaptation matrix is estimated using only a small parallel speech corpus. We refer to this method as affine NMF, and the effectiveness of this method has been confirmed by comparing its effectiveness with that of a conventional NMF-based method and a GMM-based method in noisy environments.

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Noise-Robust Voice Conversion Based on Sparse Spectral Mapping Using Non-negative Matrix Factorization

Cited by 13 publications

References 19 publications

Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition

Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition

Parallel Dictionary Learning for Multimodal Voice Conversion Using Matrix Factorization

Small-parallel exemplar-based voice conversion in noisy environments using affine non-negative matrix factorization

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