ICRCycleGAN-VC: a robust one-to-one voice conversion method based on CycleGAN and inception-ResNet blocks

A multimodal voice conversion (VC) method for noisy environments is proposed. In our previous non-negative matrix factorization (NMF)-based VC method, source and target exemplars are extracted from parallel training data, in which the same texts are uttered by the source and target speakers. The input source signal is then decomposed into source exemplars, noise exemplars, and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. In this study, we propose multimodal VC that improves the noise robustness of our NMF-based VC method. Furthermore, we introduce the combination weight between audio and visual features and formulate a new cost function to estimate audio-visual exemplars. Using the joint audio-visual features as source features, VC performance is improved compared with that of a previous audio-input exemplar-based VC method. The effectiveness of the proposed method is confirmed by comparing its effectiveness with that of a conventional audio-input NMF-based method and a Gaussian mixture model-based method.

We present in this paper an exemplar-based Voice Conversion (VC) method using Non-negative Matrix Factorization (NMF), which is different from conventional statistical VC. NMF-based VC has advantages of noise robustness and naturalness of converted voice compared to Gaussian Mixture Model (GMM)based VC. However, because NMF-based VC is based on parallel training data of source and target speakers, we cannot convert the voice of arbitrary speakers in this framework. In this paper, we propose a many-to-many VC method that makes use of Multiple Non-negative Matrix Factorization (Multi-NMF). By using Multi-NMF, an arbitrary speaker’s voice is converted to another arbitrary speaker’s voice without the need for any input or output speaker training data. We assume that this method is flexible because we can adopt it to voice quality control or noise robust VC. Index Terms: voice conversion, speech synthesis, many-tomany, exemplar-based, NMF

This paper presents a multimodal voice conversion (VC) method for noisy environments. In our previous exemplarbased VC method, source exemplars and target exemplars are extracted from parallel training data, in which the same texts are uttered by the source and target speakers. The input source signal is then decomposed into source exemplars, noise exemplars obtained from the input signal, and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. In this paper, we propose a multimodal VC method that improves the noise robustness of our previous exemplar-based VC method. As visual features, we use not only conventional DCT but also the features extracted from Active Appearance Model (AAM) applied to the lip area of a face image. Furthermore, we introduce the combination weight between audio and visual features and formulate a new cost function in order to estimate the audiovisual exemplars. By using the joint audio-visual features as source features, the VC performance is improved compared to a previous audio-input exemplar-based VC method. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method. Index Terms: voice conversion, multimodal, image features, non-negative matrix factorization, noise robustness

Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.

In recent years, voice conversion (VC) becomes a popular technique since it can be applied to various speech tasks. Most existing approaches on VC must use aligned speech pairs (parallel data) of the source speaker and the target speaker in training, which makes hard to handle it. Furthermore, VC methods proposed so far require to specify the source speaker in conversion stage, even though we just want to obtain the speech of the target speaker from the other speakers in many cases of VC. In this paper, we propose a VC method where it is not necessary to use any parallel data in the training, nor to specify the source speaker in the conversion. Our approach models a joint probability of acoustic, phonetic, and speaker features using a three-way restricted Boltzmann machine (3WRBM). Speaker-independent (SI) and speaker-dependent (SD) parameters in our model are simultaneously estimated under the maximum likelihood (ML) criteria using a speech set of multiple speakers. In conversion stage, phonetic features are at first estimated in a probabilistic manner given a speech of an arbitrary speaker, then a voice-converted speech is produced using the SD parameters of the target speaker. Our experimental results showed not only that our approach outperformed other non-parallel VC methods, but that the performance of the arbitrary-source VC was close to those of the traditional source-specified VC in our approach.

Voice conversion methods have advanced rapidly over the last decade. Studies have shown that speaker characteristics are captured by spectral feature as well as various prosodic features. Most existing conversion methods focus on the spectral feature as it directly represents the timbre characteristics, while some conversion methods have focused only on the prosodic feature represented by the fundamental frequency. In this paper, a comprehensive framework using deep neural networks to convert both timbre and prosodic features is proposed. The timbre feature is represented by a high-resolution spectral feature. The prosodic features include F0, intensity and duration. It is well known that DNN is useful as a tool to model high-dimensional features. In this work, we show that DNN initialized by our proposed autoencoder pretraining yields good quality DNN conversion models. This pretraining is tailor-made for voice conversion and leverages on autoencoder to capture the generic spectral shape of source speech. Additionally, our framework uses segmental DNN models to capture the evolution of the prosodic features over time. To reconstruct the converted speech, the spectral feature produced by the DNN model is combined with the three prosodic features produced by the DNN segmental models. Our experimental results show that the application of both prosodic and high-resolution spectral features leads to quality converted speech as measured by objective evaluation and subjective listening tests.

In this paper, we propose computationally efficient and high-quality methods for statistical voice conversion (VC) with direct waveform modification based on spectral differentials. The conventional method with a minimum-phase filter achieves high-quality conversion but requires heavy computation in filtering. This is because the minimum phase using a fixed lifter of the Hilbert transform often results in a long-tap filter. One of our methods is a data-driven method for lifter training. Since this method takes filter truncation into account in training, it can shorten the tap length of the filter while preserving conversion accuracy. Our other method is sub-band processing for extending the conventional method from narrow-band (16 kHz) to full-band (48 kHz) VC, which can convert a full-band waveform with higher converted-speech quality. Experimental results indicate that 1) the proposed lifter-training method for narrow-band VC can shorten the tap length to 1/16 without degrading the converted-speech quality and 2) the proposed sub-band-processing method for full-band VC can improve the converted-speech quality than the conventional method.

We present an unsupervised non-parallel many-to-many voice conversion (VC) method using a generative adversarial network (GAN) called StarGAN v2. Using a combination of adversarial source classifier loss and perceptual loss, our model significantly outperforms previous VC models. Although our model is trained only with 20 English speakers, it generalizes to a variety of voice conversion tasks, such as any-to-many, cross-lingual, and singing conversion. Using a style encoder, our framework can also convert plain reading speech into stylistic speech, such as emotional and falsetto speech. Subjective and objective evaluation experiments on a non-parallel many-to-many voice conversion task revealed that our model produces natural sounding voices, close to the sound quality of state-of-the-art text-to-speech (TTS) based voice conversion methods without the need for text labels. Moreover, our model is completely convolutional and with a faster-than-real-time vocoder such as Parallel WaveGAN can perform real-time voice conversion.

Several voice conversion (VC) methods using a simple autoencoder with a carefully designed information bottleneck have recently been studied. In general, they extract content information from a given speech through the information bottleneck between the encoder and the decoder, providing it to the decoder along with the target speaker information to generate the converted speech. However, their performance is highly dependent on the downsampling factor of an information bottleneck. In addition, such frame-by-frame conversion methods cannot convert speaking styles associated with the length of utterance, such as the duration. In this paper, we propose a novel duration controllable voice conversion (DCVC) model, which can transfer the speaking style and control the speed of the converted speech through a phoneme-based information bottleneck. The proposed information bottleneck does not need to find an appropriate downsampling factor, achieving a better audio quality and VC performance. In our experiments, DCVC outperformed the baseline models with a 3.78 MOS and a 3.83 similarity score. It can also smoothly control the speech duration while achieving a 39.35x speedup compared with a Seq2seq-based VC in terms of the inference speed.

Voice conversion (VC) is being widely researched in the field of speech processing because of increased interest in using such processing in applications such as personalized Text-to-Speech systems. We present in this paper a many-to-one VC method using exemplar-based sparse representation, which is different from conventional statistical VC. In our previous exemplar-based VC method, input speech was represented by the source dictionary and its sparse coefficients. The source and the target dictionaries are fully coupled and the converted voice is constructed from the source coefficients and the target dictionary. 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 this paper, we propose a many-to-one VC method in an exemplar-based framework which does not need training data of the source speaker. Some statistical approaches for many-to-one VC have been proposed; however, in the framework of exemplar-based VC, such a method has never been proposed. The effectiveness of our many-to-one VC has been confirmed by comparing its effectiveness with that of a conventional one-to-one NMF-based method and one-to-one GMM-based method.

Voice conversion can be reduced to a problem to find a transformation function between the corresponding speech sequences of two speakers. Perhaps the most voice conversions methods are GMM-based statistical mapping methods [1, 2]. However, the classical GMM-based mapping is frame-to-frame, and cannot take account of the contextual information existing over a speech sequence. It is well known that HMM yields an efficient method to model the density of a whole speech sequence and has found great successes in speech recognition and synthesis. Inspired by this fact, this paper studies how to use HMM for voice conversion. We derive an HMM-based sequence-to-frame mapping function with statistical analysis. Different from previous HMM-based voice conversion methods [3, 4, 5] that used forced alignment for segmentation and transform frames aligned to a state with its associated linear transformation, our method has a soft mapping function as a weighted summation of linear transformations. The weights are calculated as the HMM posterior probabilities of frames. We also propose and compare two methods to learn the parameters of our mapping functions, namely least square error estimation and maximum likelihood estimation. We carried out experiments to examine the proposed HMM-based method for voice conversion.

We investigate a structured sparse spectral transform method for voice conversion (VC) to perform frequency warping and spectral shaping simultaneously on high-dimensional (D) STRAIGHT spectra. Learning a large transform matrix for high-D data often results in an overfit matrix with low sparsity, which leads to muffled speech in VC. We address this problem by using the frequency-warping characteristic of a source-target speaker pair to define a region of support (ROS) in a transform matrix, and further optimize it by nonnegative matrix factorization (NMF) to obtain structured sparse transform. We also investigate structural measures of spectral and temporal covariance and variance at different scales for assessing VC speech quality. Our experiments on ARCTIC dataset of 12 speaker pairs show that embedding the ROS in spectral transforms offers flexibility in tradeoffs between spectral distortion and structure preservation, and the structural measures provide quantitatively reasonable results on converted speech. Our subjective listening tests show that the proposed VC method achieves a mean opinion score of "very good" relative to natural speech, and in comparison with three other VC methods, it is the most preferred one in naturalness and in voice similarity to target speakers.

This paper presents a multimodal voice conversion (VC) method for noisy environments. In our previous NMF-based VC method, source exemplars and target exemplars are extracted from parallel training data, in which the same texts are uttered by the source and target speakers. The input source signal is then decomposed into source exemplars, noise exemplars obtained from the input signal, and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. In this paper, we propose a multimodal VC that improves the noise robustness in our NMF-based VC method. By using the joint audio-visual features as source features, the performance of VC is improved compared to a previous audio-input NMF-based VC method. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method.

We present in this paper an exemplar-based voice conversion (VC) method using a phoneme-categorized dictionary. Sparse representation-based VC using Non-negative matrix factorization (NMF) is employed for spectral conversion between different speakers. In our previous NMF-based VC method, source exemplars and target exemplars are extracted from parallel training data, having the same texts uttered by the source and target speakers. The input source signal is represented using the source exemplars and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. However, this exemplar-based approach needs to hold all the training exemplars (frames), and it may cause mismatching of phonemes between input signals and selected exemplars. In this paper, in order to reduce the mismatching of phoneme alignment, we propose a phoneme-categorized sub-dictionary and a dictionary selection method using NMF. By using the sub-dictionary, the performance of VC is improved compared to a conventional NMF-based VC. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method and a conventional NMF-based method.

This article puts forward a new algorithm for voice conversion which not only removes the necessity of parallel corpus in the training phase but also resolves the issue of insufficiency of the target speaker’s corpus. The proposed approach is based on one of the new voice conversion models utilizing classical LPC analysis-synthesis model combined with GMM. Through this algorithm, the conversion functions among vowels and demi-syllables are derived. We assumed that these functions are rather the same for different speakers if their genders, accents, and languages are alike. Therefore, we will be able to produce the demi-syllables with just having access to few sentences from the target speaker and forming the GMM for one of his/her vowels. The results from the appraisal of the proposed method for voice conversion clarifies that this method has the ability to efficiently realize the speech features of the target speaker. It can also provide results comparable to the ones obtained through the parallel-corpus-based approaches.