We propose a non-parallel voice-conversion (VC) method that can learn a mapping from source to target speech without relying on parallel data. The proposed method is particularly noteworthy in that it is general purpose and high quality and works without any extra data, modules, or alignment procedure. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial network (CycleGAN) with gated convolutional neural networks (CNNs) and an identity-mapping loss. A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses. This makes it possible to find an optimal pseudo pair from non-parallel data. Furthermore, the adversarial loss can bring the converted speech close to the target one on the basis of indistinguishability without explicit density estimation. This allows to avoid over-smoothing caused by statistical averaging, which occurs in many conventional statistical model-based VC methods that represent data distribution explicitly. We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss. This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information. We evaluated our method on a non-parallel VC task. An objective evaluation showed that the converted feature sequence was near natural in terms of global variance and modulation spectra, which are structural indicators highly correlated with subjective evaluation. A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based parallel VC method even though CycleGAN-VC is trained under disadvantageous conditions (non-parallel and half the amount of data).

CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks

This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate converted speech signals quickly enough to allow real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.

StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks

This paper presents a new spectral envelope conversion method using deep neural networks (DNNs). The conventional joint density Gaussian mixture model (JDGMM) based spectral conversion methods perform stably and effectively. However, the speech generated by these methods suffer severe quality degradation due to the following two factors: 1) inadequacy of JDGMM in modeling the distribution of spectral features as well as the non-linear mapping relationship between the source and target speakers, 2) spectral detail loss caused by the use of high-level spectral features such as mel-cepstra. Previously, we have proposed to use the mixture of restricted Boltzmann machines (MoRBM) and the mixture of Gaussian bidirectional associative memories (MoGBAM) to cope with these problems. In this paper, we propose to use a DNN to construct a global non-linear mapping relationship between the spectral envelopes of two speakers. The proposed DNN is generatively trained by cascading two RBMs, which model the distributions of spectral envelopes of source and target speakers respectively, using a Bernoulli BAM (BBAM). Therefore, the proposed training method takes the advantage of the strong modeling ability of RBMs in modeling the distribution of spectral envelopes and the superiority of BAMs in deriving the conditional distributions for conversion. Careful comparisons and analysis among the proposed method and some conventional methods are presented in this paper. The subjective results show that the proposed method can significantly improve the performance in terms of both similarity and naturalness compared to conventional methods.

Voice conversion using deep neural networks with layer-wise generative training

An overview of voice conversion systems

Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature.

Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

In this paper, we present statistical approaches to enhance body-conducted unvoiced speech for silent speech communication. A body-conductive microphone called nonaudible murmur (NAM) microphone is effectively used to detect very soft unvoiced speech such as NAM or a whispered voice while keeping speech sounds emitted outside almost inaudible. However, body-conducted unvoiced speech is difficult to use in human-to-human speech communication because it sounds unnatural and less intelligible owing to the acoustic change caused by body conduction. To address this issue, voice conversion (VC) methods from NAM to normal speech (NAM-to-Speech) and to a whispered voice (NAM-to-Whisper) are proposed, where the acoustic features of body-conducted unvoiced speech are converted into those of natural voices in a probabilistic manner using Gaussian mixture models (GMMs). Moreover, these methods are extended to convert not only NAM but also a body-conducted whispered voice (BCW) as another type of body-conducted unvoiced speech. Several experimental evaluations are conducted to demonstrate the effectiveness of the proposed methods. The experimental results show that 1) NAM-to-Speech effectively improves intelligibility but it causes degradation of naturalness owing to the difficulty of estimating natural fundamental frequency contours from unvoiced speech; 2) NAM-to-Whisper significantly outperforms NAM-to-Speech in terms of both intelligibility and naturalness; and 3) a single conversion model capable of converting both NAM and BCW is effectively developed in our proposed VC methods.

M. Nakagiri

Papers

Statistical Voice Conversion Techniques for Body-Conducted Unvoiced Speech Enhancement