Spectral Tilt May Have a Smaller Impact on the Intelligibility of Speech in Noise
Yoshiki Sato, Julián Villegas · 2023
We compared spectral tilt modifications of plain speech made by Linear Predictive Coding—LPC transplantation and fractional roll-off filtering—FRF. These modifications were done at utterance-, phone-, or frame-level to equate the spectral tilt of the same utterances produced in noise (Lombard speech) by the same speakers. When mixed with speech-shaped noise at the same Signal to Noise Ratio (SNR), speech treated with LPC transplantation yielded larger objective intelligibility gains relative to those of speech treated with FRF. However, it also yielded larger spectral tilt errors for frame-based modifications. This finding suggests that the hitherto spectral tilt benefits assigned to Lombard speech may be smaller than previously thought, especially for those reports based on phone- or frame-based LPC transplantation.