  In this work we evaluate a neural based speech intelligibility booster based
on spectral shaping and dynamic range compression (SSDRC), referred to as
WaveNet-based SSDRC (wSSDRC), using a recently designed Greek Harvard-style
corpus. The corpus has been developed according to the format of the
Harvard/IEEE sentences and offers the opportunity to apply neural speech
enhancement models and examine their performance gain for Greek listeners.
wSSDRC has been successfully tested for English material and speakers in the
past. In this paper we revisit wSSDRC to perform a full scale evaluation of the
model with Greek listeners under the condition of equal energy before and after
modification. Both normal hearing (NH) and hearing impaired (HI) listeners
evaluated the model under speech shaped noise (SSN) at listener-specific SNRs
matching their Speech Reception Threshold (SRT) - a point at which 50 % of
unmodified speech is intelligible. The analysis statistics show that the wSSDRC
model has produced a median intelligibility boost of 39% for NH and 38% for HI,
relative to the plain unprocessed speech.
