Emotional voice conversion using deep neural networks with MCC and F0 features
By Zhaojie Luo, Tetsuya Takiguchi, Yasuo Ariki. 2016
45 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wei-Ning Hsu
- Yossi Adi
- Emmanuel Dupoux
- Jade Copet
- Tu Nguyen
- Evgeny Kharitonov
- Abdel-rahman Mohamed
- Adam Polyak
In Signal Processing
- Wei-Ning Hsu
- Yossi Adi
- Emmanuel Dupoux
- Jade Copet
- Tu Nguyen
- Abdel-rahman Mohamed
- Evgeny Kharitonov
- Adam Polyak
Citations in the corpus
- The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems (2018, positive): “The procedure of this experiment is the same as in [17] which showed good results for emotion to neutral speech transformations.”
- Emotional Voice Conversion: Theory, Databases and ESD (2021, background): “DNN [16], deep belief network (DBN) [39], highway neural network [41], and DBLSTM [42] are examples that perform spectrum and prosody mapping with parallel training utterances.”
- Textless Speech Emotion Conversion using Decomposed and Discrete Representations (2021, methodology): “This include using multilayer perceptron (Luo et al., 2016; Lorenzo-Trueba et al., 2018), highway neural network (Shankar et al.”