  Cochlear implant (CI) users have considerable difficulty in understanding
speech in reverberant listening environments. Time-frequency (T-F) masking is a
common technique that aims to improve speech intelligibility by multiplying
reverberant speech by a matrix of gain values to suppress T-F bins dominated by
reverberation. Recently proposed mask estimation algorithms leverage machine
learning approaches to distinguish between target speech and reverberant
reflections. However, the spectro-temporal structure of speech is highly
variable and dependent on the underlying phoneme. One way to potentially
overcome this variability is to leverage explicit knowledge of phonemic
information during mask estimation. This study proposes a phoneme-based mask
estimation algorithm, where separate mask estimation models are trained for
each phoneme. Sentence recognition tests were conducted in normal hearing
listeners to determine whether a phoneme-based mask estimation algorithm is
beneficial in the ideal scenario where perfect knowledge of the phoneme is
available. The results showed that the phoneme-based masks improved the
intelligibility of vocoded speech when compared to conventional
phoneme-independent masks. The results suggest that a phoneme-based speech
enhancement strategy may potentially benefit CI users in reverberant listening
environments.
