A GMM based residual prediction method for voice conversion

In conventional voice conversion methods, some features of a speech signal’s spectrum envelope are first extracted. Then, these features are converted so as to best match a target speaker’s speech by designing and using a set of conversions. Ultimately, the spectrum envelope of the target speaker’s speech signal is reconstructed from the converted features. The spectrum envelope reconstructed from the converted features usually deviates from its natural form. This aberration from the natural form observed in cases such as over-smoothing, over-fitting, and widening of formants is partially caused by two factors: (1) there is an error in the reconstruction of spectrum envelope from the features, and (2) the set of features extracted from the spectrum envelope of the speech signal is not closed. A method is put forward to improve the naturalness of speech by means of \(\epsilon \)-closed sets of extended vectors in voice conversion systems. In this approach, \(\epsilon \)-closed sets to reconstruct the natural spectrum envelope of a signal in the synthesis phase are introduced. The elements of these sets are generated by forming a group of extended vectors of features and applying a quantization scheme on the features of a speech signal. The use of this method in speech synthesis leads to a noticeable reduction of error in spectrum reconstruction from the features. Furthermore, the final spectrum envelope extracted from voice conversions maintains its natural form and, consequently, the problems arising from the deviation of voice from its natural state are resolved. The above method can be generally used as one phase of speech synthesis. It is independent of the voice conversion technique used and its parallel or non-parallel training method, and can be applied to improve the naturalness of the generated speech signal in all common voice conversion methods. Moreover, this method can be used in other fields of speech processing like texts to speech systems and vocoders to improve the quality of the output signal in the synthesis step.

Laryngectomees are individuals whose larynx has been surgically removed, usually due to laryngeal cancer. The immediate consequence of this operation is that these individuals (laryngectomees) are unable to speak. Esophageal speech (ES) remains the preferred alternative speaking method for laryngectomees. However, compared to the laryngeal voice, ES is characterized by low intelligibility and poor quality due to chaotic fundamental frequency F0, specific noises, and low intensity. Our proposal to solve these problems is to take advantage of voice conversion as an effective way to improve speech quality and intelligibility. To this end, we propose in this work a novel esophageal–laryngeal voice conversion (VC) system based on a sequence-to-sequence (Seq2Seq) model combined with an auditory attention mechanism. The originality of the proposed framework is that it adopts an auditory attention technique in our model, which leads to more efficient and adaptive feature mapping. In addition, our VC system does not require the classical DTW alignment process during the learning phase, which avoids erroneous mappings and significantly reduces the computational time. Moreover, to preserve the identity of the target speaker, the excitation and phase coefficients are estimated by querying a binary search tree. In experiments, objective and subjective tests confirmed that the proposed approach performs better even in some difficult cases in terms of speech quality and intelligibility.

We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker. By using a multi-speaker speech corpus to train all requisite encoders and decoders in the initial training stage, our system can clone unseen voices using untranscribed speech of target speakers on the basis of the backpropagation algorithm. Moreover, depending on the data circumstance of the target speaker, the cloning strategy can be adjusted to take advantage of additional data and modify the behaviors of text-to-speech (TTS) and/or voice conversion (VC) systems to accommodate the situation. We test the performance of the proposed framework by using deep convolution layers to model the encoders, decoders and WaveNet vocoder. Evaluations show that it achieves comparable quality with state-of-the-art TTS and VC systems when cloning with just five minutes of untranscribed speech. Moreover, it is demonstrated that the proposed framework has the ability to switch between TTS and VC with high speaker consistency, which will be useful for many applications.

Phonetic posteriorgram-based voice conversion system to improve speech intelligibility of dysarthric patients

In this paper we deal with initial experiments on creating a new Czech voice conversion system. Voice conversion (VC) is a process which modifies the speech signal produced by one (source) speaker so that it sounds like another (target) speaker. Using VC technique a new voice for speech synthesizer can be prepared with no need to record a huge amount of new speech data. The transformation is determined using equal sentences from both speakers; these sentences are time-aligned using modified dynamic time warping algorithm. The conversion is divided into two stages corresponding to the source-filter model of speech production. Within this work we employ conversion function based on Gaussian mixture model for transforming the spectral envelope described by line spectral frequencies. Residua are converted using so called residual prediction techniques. Unlike in other similar research works, we predict residua not from the transformed spectral envelope, but directly from the source speech. Four versions of residual prediction are described and compared in this study. Objective evaluation of converted speech using performance metrics shows that our system is comparable with similar existing VC systems.KeywordsGaussian Mixture ModelDynamic Time WarpingResidual SignalSpeech DataSource SpeechThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper presents an alternative voice conversion technique using support vector machine (SVM)-regression as a tool which converts a source speaker’s voice to specific standard target speaker. The main objective of the work is to capture a nonlinear mapping function between the parameters for the acoustic features of the two speakers. Line spectral frequencies (LSFs) have been used as features to represent the vocal tract characteristics. We use kernel induced feature space with radial basis function network (RBFN) type SVM that uses gaussian kernel. The intonation characteristics (pitch contour) is modified using the baseline technique, i.e. gaussian normalization. The transformed LSFs along with the modified pitch contour are used to synthesize the speech signal for the desired target speaker. The target speaker’s speech signal is synthesized and evaluated using both the subjective and the listening tests. The results signify that the proposed model improves the voice conversion performance in terms of capturing the speaker’s identity as compared to our previous approach. In the previous approach we used feed forward neural network (FFNN) based model for vocal tract modification and codebook based method for pitch contour modification. However, the performance of the proposed system can further be improved by suitably modifying various user-defined parameters used in regression analysis and using more training LSF vectors in the training stage.

The framework of voice conversion system is expected to emphasize both the static and dynamic characteristics of the speech signal. The conventional approaches like Mel frequency cepstrum coefficients and linear predictive coefficients focus on spectral features limited to lower frequency bands. This paper presents a novel wavelet packet filter bank approach to identify non-uniformly distributed dynamic characteristics of the speaker. Contribution of this paper is threefold. First, in the feature extraction stage, dyadic wavelet packet tree structure is optimized to involve less computation while preserving the speaker-specific features. Second, in the feature representation step, magnitude and phase attributes are treated separately to rule out on the fact that raw time-frequency traits are highly correlated but carry intelligent speech information. Finally, the RBF mapping function is established to transform the speaker-specific features from the source to the target speakers. The results obtained by the proposed filter bank-based voice conversion system are compared to the baseline multiscale voice morphing results by using subjective and objective measures. Evaluation results reveal that the proposed method outperforms by incorporating the speaker-specific dynamic characteristics and phase information of the speech signal.

An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person’s speech signal to make it sound like another speaker’s voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofing attacks are designed to discriminate bona fide speech from spoofed speech for speaker verification systems. In this paper, we investigate the problem of source speaker identification – inferring the identity of the source speaker given the voice converted speech. To perform source speaker identification, we simply add voice-converted speech data with the label of source speaker identity to the genuine speech dataset during speaker embedding network training. Experimental results show the feasibility of source speaker identification when training and testing with converted speeches from the same voice conversion model(s). In addition, our results demonstrate that having more converted utterances from various voice conversion model for training helps improve the source speaker identification performance on converted utterances from unseen voice conversion models.

Voice conversion is a challenging task which transforms the voice characteristics of a source speaker to a target speaker without changing linguistic content. Recently, there have been many works on many-to-many Voice Conversion (VC) based on Variational Autoencoder (VAEs) achieving good results, however, these methods lack the ability to disentangle speaker identity and linguistic content to achieve good performance on unseen speaker scenarios. In this paper, we propose a new method based on feature disentanglement to tackle many to many voice conversion. The method has the capability to disentangle speaker identity and linguistic content from utterances, it can convert from many source speakers to many target speakers with a single autoencoder network. Moreover, it naturally deals with the unseen target speaker scenarios. We perform both objective and subjective evaluations to show the competitive performance of our proposed method compared with other state-of-the-art models in terms of naturalness and target speaker similarity.

Voice conversion is to transform a source speaker to the target one, while keeping the linguistic content unchanged. Recently, one-shot voice conversion gradually becomes a hot topic for its potentially wide range of applications, where it has the capability to convert the voice from any source speaker to any other target speaker even when both the source speaker and the target speaker are unseen during training. Although a great progress has been made in one-shot voice conversion, the naturalness of the converted speech remains a challenging problem. To further improve the naturalness of the converted speech, this paper proposes a two-level nested U-structure (U2-Net) voice conversion algorithm called U2-VC. The U2-Net can extract both local feature and multi-scale feature of log-mel spectrogram, which can help to learn the time-frequency structures of the source speech and the target speech. Moreover, we adopt sandwich adaptive instance normalization (SaAdaIN) in decoder for speaker identity transformation to retain more content information of the source speech while maintaining the speaker similarity between the converted speech and the target speech. Experiments on VCTK dataset show that U2-VC outperforms many SOTA approaches including AGAIN-VC and AdaIN-VC in terms of both objective and subjective measurements.

Owing to the recent advancements in deep learning technology, the performance of voice conversion (VC) in terms of quality and similarity has significantly improved. However, complex computation is generally required for deep-learning-based VC systems. This can cause a notable latency, which limits the deployment of such VC systems in real-world applications. Therefore, increasing the efficiency of online computing has become an important task. In this study, we propose a novel mixture-of-experts (MoE) based VC system, termed MoEVC. The MoEVC system uses a gating mechanism to assign weights to feature maps to increase VC performance. In addition, applying sparse constraints on the gating mechanism can skip some convolution processes through elimination of redundant feature maps, thereby accelerating online computing. Experimental results show that by using proper sparse constraints, we can effectively reduce the FLOPs (floating-point operations) count by 70%, while improving VC performance in both objective evaluation and human subjective listening tests.

Voice conversion with SI-DNN and KL divergence based mapping without parallel training data

In this paper, we present a voice conversion (VC) method that does not use any parallel data while training the model. VC is a technique where only speaker-specific information in source speech is converted while keeping the phonological information unchanged. Most of the existing VC methods rely on parallel data—pairs of speech data from the source and target speakers uttering the same sentences. However, the use of parallel data in training causes several problems: 1) the data used for the training are limited to the predefined sentences, 2) the trained model is only applied to the speaker pair used in the training, and 3) mismatches in alignment may occur. Although it is, thus, fairly preferable in VC not to use parallel data, a nonparallel approach is considered difficult to learn. In our approach, we achieve nonparallel training based on a speaker adaptation technique and capturing latent phonological information. This approach assumes that speech signals are produced from a restricted Boltzmann machine-based probabilistic model, where phonological information and speaker-related information are defined explicitly. Speaker-independent and speaker-dependent parameters are simultaneously trained under speaker adaptive training. In the conversion stage, a given speech signal is decomposed into phonological and speaker-related information, the speaker-related information is replaced with that of the desired speaker, and then voice-converted speech is obtained by mixing the two. Our experimental results showed that our approach outperformed another nonparallel approach, and produced results similar to those of the popular conventional Gaussian mixture models-based method that used parallel data in subjective and objective criteria.

In this paper, we present the voice conversion (VC) systems developed at Nagoya University (NU) for the Voice Conversion Challenge 2020 (VCC2020). We aim to determine the effectiveness of two recent significant technologies in VC: sequence-to-sequence (seq2seq) models and autoregressive (AR) neural vocoders. Two respective systems were developed for the two tasks in the challenge: for task 1, we adopted the Voice Transformer Network, a Transformer-based seq2seq VC model, and extended it with synthetic parallel data to tackle nonparallel data; for task 2, we used the frame-based cyclic variational autoencoder (CycleVAE) to model the spectral features of a speech waveform and the AR WaveNet vocoder with additional fine-tuning. By comparing with the baseline systems, we confirmed that the seq2seq modeling can improve the conversion similarity and that the use of AR vocoders can improve the naturalness of the converted speech.

We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting. Most prior work require either parallel speech corpora or enough amount of training data from a target speaker. However, we convert an arbitrary sentences of an arbitrary source speaker to target speaker's given only one target speaker training utterance. To achieve this, we formulate the problem as learning disentangled speaker-specific and context-specific representations and follow the idea of [1] which uses Factorized Hierarchical Variational Autoencoder (FHVAE). After training FHVAE on multi-speaker training data, given arbitrary source and target speakers' utterance, we estimate those latent representations and then reconstruct the desired utterance of converted voice to that of target speaker. We investigate the effectiveness of the approach by conducting voice conversion experiments with varying size of training utterances and it was able to achieve reasonable performance with even just one training utterance. We also examine the speech representation and show that World vocoder outperforms Short-time Fourier Transform (STFT) used in [1]. Finally, in the subjective tests, for one language and cross-lingual voice conversion, our approach achieved significantly better or comparable results compared to VAE-STFT and GMM baselines in speech quality and similarity.