“Flow-based Speech Analysis”版本间的差异
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===Publications=== | ===Publications=== | ||
− | * Haoran Sun, Yunqi Cai, Lantian Li, Dong Wang, "On Investigation of Unsupervised Speech Factorization Based in Normalization Flow", 2019. [[文件:Flow.pdf|pdf] | + | * Haoran Sun, Yunqi Cai, Lantian Li, Dong Wang, "On Investigation of Unsupervised Speech Factorization Based in Normalization Flow", 2019. [[文件:Flow.pdf|pdf]] |
===Source Code=== | ===Source Code=== |
2019年10月29日 (二) 12:57的版本
Introduction
- We present a preliminary investigation on unsupervised speech factorization based on the normalization flow model.
This model constructs a complex invertible transform, by which we can project speech segments into a latent code space where the distribution is a simple diagonal Gaussian.
- Our preliminary investigation on the TIMIT database shows that this code space exhibits favorable properties such as denseness and pseudo linearity,
and perceptually important factors such as phonetic content and speaker trait can be represented as particular directions within the code space.
- Index Terms: speech factorization, normalization flow, deep learning
Members
- Dong Wang, Haoran Sun, Yunqi Cai, Lantian Li
Publications
- Haoran Sun, Yunqi Cai, Lantian Li, Dong Wang, "On Investigation of Unsupervised Speech Factorization Based in Normalization Flow", 2019. pdf
Source Code
- Glow model implemented by Yuki-Chai in PyTorch. glow-pytorch
Experiments
- Right click "figs" or "wavs" and select "save as" to save the spectrogram figures or related audio files.
Future Work
- To conduct more thorough studies on large databases and continuous speech.
- To investigate discriminative flow models which take class information into consideration.