  Speech phase prediction, which is a significant research focus in the field
of signal processing, aims to recover speech phase spectra from
amplitude-related features. However, existing speech phase prediction methods
are constrained to recovering phase spectra with short frame shifts, which are
considerably smaller than the theoretical upper bound required for exact
waveform reconstruction of short-time Fourier transform (STFT). To tackle this
issue, we present a novel long-frame-shift neural speech phase prediction
(LFS-NSPP) method which enables precise prediction of long-frame-shift phase
spectra from long-frame-shift log amplitude spectra. The proposed method
consists of three stages: interpolation, prediction and decimation. The
short-frame-shift log amplitude spectra are first constructed from
long-frame-shift ones through frequency-by-frequency interpolation to enhance
the spectral continuity, and then employed to predict short-frame-shift phase
spectra using an NSPP model, thereby compensating for interpolation errors.
Ultimately, the long-frame-shift phase spectra are obtained from
short-frame-shift ones through frame-by-frame decimation. Experimental results
show that the proposed LFS-NSPP method can yield superior quality in predicting
long-frame-shift phase spectra than the original NSPP model and other
signal-processing-based phase estimation algorithms.
