Abstract
Decomposition of speech into a deterministic part and a stochastic part is a typical modeling. Usually, the deterministic part in voiced speech is modeled as a sum of time-varying sinusoids while the stochastic part is modeled as modulated noise. The estimation of sinusoidal parameters assumes that locally speech is a stationary signal. However, this is not true leading to biased amplitude and phase estimation. In this paper, we develop a scheme for speech analysis and synthesis which is able to deal with locally nonstationary frames. Thus, deterministic part it modeled using an adaptive quasi-harmonic model while stochastic part is modeled as time-modulated and frequency-modulated noise. Results show that the reconstructed signal is almost indistinguishable from the original.
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