  Availability of representations from pre-trained models (PTMs) have
facilitated substantial progress in speech emotion recognition (SER).
Particularly, representations from PTM trained for paralinguistic speech
processing have shown state-of-the-art (SOTA) performance for SER. However,
such paralinguistic PTM representations haven't been evaluated for SER in
linguistic environments other than English. Also, paralinguistic PTM
representations haven't been investigated in benchmarks such as SUPERB,
EMO-SUPERB, ML-SUPERB for SER. This makes it difficult to access the efficacy
of paralinguistic PTM representations for SER in multiple languages. To fill
this gap, we perform a comprehensive comparative study of five SOTA PTM
representations. Our results shows that paralinguistic PTM (TRILLsson)
representations performs the best and this performance can be attributed to its
effectiveness in capturing pitch, tone and other speech characteristics more
effectively than other PTM representations.
