Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities

Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.Comment: Accepted to INTERSPEECH 201

Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201

Cross-Lingual Voice Conversion with Non-Parallel Data

In this project a Phonetic Posteriorgram (PPG) based Voice Conversion system is implemented. The main goal is to perform and evaluate conversions of singing voice. The cross-gender and cross-lingual scenarios are considered. Additionally, the use of spectral envelope based MFCC and pseudo-singing dataset for ASR training are proposed in order to improve the performance of the system in the singing context

Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings

The paper presents a novel architecture and method for speech synthesis in multiple languages, in voices of multiple speakers and in multiple speaking styles, even in cases when speech from a particular speaker in the target language was not present in the training data. The method is based on the application of neural network embedding to combinations of speaker and style IDs, but also to phones in particular phonetic contexts, without any prior linguistic knowledge on their phonetic properties. This enables the network not only to efficiently capture similarities and differences between speakers and speaking styles, but to establish appropriate relationships between phones belonging to different languages, and ultimately to produce synthetic speech in the voice of a certain speaker in a language that he/she has never spoken. The validity of the proposed approach has been confirmed through experiments with models trained on speech corpora of American English and Mexican Spanish. It has also been shown that the proposed approach supports the use of neural vocoders, i.e. that they are able to produce synthesized speech of good quality even in languages that they were not trained on