voice conversion

Siamese Autoencoders for Speech Style Extraction and Switching Applied to Voice Identification and Conversion Interspeech 2017 Speaker Dependent Approach for Enhancing a Glossectomy Patient’s Speech via GMM-Based Voice Conversion Interspeech 2017 Speaking Style Conversion from Normal to Lombard Speech Using a Glottal Vocoder and Bayesian GMMs Interspeech 2017 Spectro-Temporal Modelling with Time-Frequency LSTM and Structured Output Layer for Voice Conversion Interspeech 2017 Statistical Voice Conversion with WaveNet-Based Waveform Generation Interspeech 2017 Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks Interspeech 2017 Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities Interspeech 2017 A KL Divergence and DNN-Based Approach to Voice Conversion without Parallel Training Sentences Interspeech 2016 Analysis of the Voice Conversion Challenge 2016 Evaluation Results Interspeech 2016 Applying Spectral Normalisation and Efficient Envelope Estimation and Statistical Transformation for the Voice Conversion Challenge 2016 Interspeech 2016 A Voice Conversion Mapping Function Based on a Stacked Joint-Autoencoder Interspeech 2016 Comparing Articulatory and Acoustic Strategies for Reducing Non-Native Accents Interspeech 2016 Deep Bidirectional LSTM Modeling of Timbre and Prosody for Emotional Voice Conversion Interspeech 2016 Generative Acoustic-Phonemic-Speaker Model Based on Three-Way Restricted Boltzmann Machine Interspeech 2016 Locally Linear Embedding for Exemplar-Based Spectral Conversion Interspeech 2016 ML Parameter Generation with a Reformulated MGE Training Criterion — Participation in the Voice Conversion Challenge 2016 Interspeech 2016 Parallel Dictionary Learning for Voice Conversion Using Discriminative Graph-Embedded Non-Negative Matrix Factorization Interspeech 2016 The NU-NAIST Voice Conversion System for the Voice Conversion Challenge 2016 Interspeech 2016 The USTC System for Voice Conversion Challenge 2016: Neural Network Based Approaches for Spectrum, Aperiodicity and F0Conversion Interspeech 2016 The Voice Conversion Challenge 2016 Interspeech 2016 Voice Conversion Based on Matrix Variate Gaussian Mixture Model Using Multiple Frame Features Interspeech 2016 Voice Conversion Based on Trajectory Model Training of Neural Networks Considering Global Variance Interspeech 2016