In this study, we trained a deep autoencoder to build compact representations of short-term spectra of multiple speakers.

VOICE CONVERSION USING DEEP NEURAL NETWORKS. WITH SPEAKER-INDEPENDENT PRE-TRAINING. Seyed Hamidreza Mohammadi, Alexander Kain. Oregon Health & Science ...

May 16, 2015 · In this study, we trained a deep autoencoder to build compact representations of short-term spectra of multiple speakers. Using this compact ...

This is first ever DNN using pretaining for Voice conversion. This is contain Simple DNN with Deep Autoencoder ans Artificial Neural Network.

ABSTRACT. In this study, we trained a deep autoencoder to build compact rep- resentations of short-term spectra of multiple speakers. Using this.

Voice conversion using deep neural networks with speaker-independent pre-training · Computer Science. 2014 IEEE Spoken Language Technology Workshop… · 2014.

Feb 27, 2017 · The speaker independent DNN model is capable to learn the transformation from training speaker features to a speaker-independent feature space.

Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.

The speaker independent DNN model is capable to learn the transformation from training speaker features to a speaker-independent feature space. For a new ...

Feb 28, 2023 · This article provides a comprehensive introduction to the latest advancements in voice conversion using neural networks.