In the state-of-the-art statistical parametric speech synthesis system, a speech analysis module, e.g. STRAIGHT spectral analysis, is generally used for obtaining accurate and stable spectral envelopes, and then low-dimensional acoustic features extracted from obtained spectral envelopes are used for training acoustic models. However, a spectral envelope estimation algorithm used in such a speech analysis module includes various processing derived from human knowledge. In this paper, we present our investigation of deep autoencoder based, non-linear, data-driven and unsupervised lowdimensional feature extraction using FFT spectral envelopes for statistical parametric speech synthesis. Experimental results showed that a text-to-speech synthesis system using deep auto-encoder based low-dimensional feature extraction from FFT spectral envelopes is indeed a promising approach.
In REF , an autoencoder based, non-linear and data-driven method is used to extract low-dimensional features from the FFT spectral envelop, instead of using the speech analysis module based on human knowledge.
A deep auto-encoder based low-dimensional feature extraction from FFT spectral envelopes for statistical parametric speech synthesis
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science