A new model-based speech analysis/Synthesis system

By Daniel W. Griffin, Jae S. Lim. 1985

Subfields: Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Speech & Audio

1. Yossi Adi
2. Wei-Ning Hsu
3. Jade Copet
4. Gabriel Synnaeve
5. Emmanuel Dupoux
6. Abdel-rahman Mohamed
7. Alexandre D'efossez
8. Evgeny Kharitonov

Citations in the corpus

• High Fidelity Neural Audio Compression (2022, background): “Despite some advances (Griffin & Lim, 1985; McCree et al., 1996), modeling the excitation signal has remained a challenging task.”
• Speech Resynthesis from Discrete Disentangled Self-Supervised Representations (2021, background): “Despite some advances [28, 29], modeling the excitation signal has remained a challenge.”
• LPCNET: Improving Neural Speech Synthesis through Linear Prediction (2018, background): “Despite some advances [10, 11], modeling the excitation signal has remained a challenge.”
• A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet (2019, background): “Despite some advances [3, 4, 5], modeling the excitation signal has remained a challenge.”
• Neural Source-Filter Waveform Models for Statistical Parametric Speech Synthesis (2019, methodology): “Although spectral amplitude distance has been used in classical speech coding methods [30]–[32], we further compute multiple distances with different short-time analysis configurations, as illustrated in Fig.”