Product of Experts for Statistical Parametric Speech Synthesis

By Heiga Zen, Mark John Francis Gales, Yoshihiko Nankaku, Keiichi Tokuda. 2012

56 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

1. Dong Yu
2. Heiga Zen
3. Graham Neubig
4. Li Deng
5. Junichi Yamagishi
6. Jinyu Li
7. Yoshua Bengio
8. Yu Zhang

In Signal Processing

1. Dong Yu
2. Li Deng
3. Heiga Zen
4. Junichi Yamagishi
5. Jinyu Li
6. Andrew W. Senior
7. Tomoki Toda
8. Sakriani Watiasri Sakti

Citations in the corpus

• Deep Learning: Methods and Applications (2014, background): “Similar trajectory HMMs also form the basis for parametric speech synthesis [228, 326, 439, 438].”
• A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “Similar trajectory HMMs also form the basis for parametric speech synthesis (Zen et al., 2011; Zen et al., 2012; Ling et al., 2013; Shannon et al., 2013).”
• Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis (2014, background): “There have been many attempts to improve the accuracy, such as trajectory HMMs [4], autoregressive HMMs [5], minimum generation error (MGE) training [6], product of experts (PoEs) [7,8], Gaussian process regression (GPR) [9], and restricted Boltzmann machines (RBMs) [10, 11].”
• Postfilters to Modify the Modulation Spectrum for Statistical Parametric Speech Synthesis (2016, background): “(5), where P ( W y | q , X , λ ) is derived from the GMM.”
• Deep Neural Networks for Acoustic Modeling in Speech Recognition (2012, unknown): “, [41].”
• Speech Synthesis Based on Hidden Markov Models (2013, unknown): “The relationship between the trajectory HMM and the Markov random field was also discussed [129].”
• Machine Learning Paradigms for Speech Recognition: An Overview (2013, methodology): “The undirected form of graphical models, including Markov random field and the product of experts model as its special case, has been applied successfully in HMM-based parametric speech synthesis research and systems [59].”
• Statistical parametric speech synthesis using deep neural networks (2013, background): “1 Boosting [25], tree intersections [19, 26, 27], or product of decision tree-clustered experts [28] add one level to the base learner (i.”