  Self-supervised learning (SSL) for automated speech recognition in terms of
its emotional content, can be heavily degraded by the presence noise, affecting
the efficiency of modeling the intricate temporal and spectral informative
structures of speech. Recently, SSL on large speech datasets, as well as new
audio-specific SSL proxy tasks, such as, temporal and frequency masking, have
emerged, yielding superior performance compared to classic approaches drawn
from the image augmentation domain. Our proposed contribution builds upon this
successful paradigm by introducing CochCeps-Augment, a novel bio-inspired
masking augmentation task for self-supervised contrastive learning of speech
representations. Specifically, we utilize the newly introduced bio-inspired
cochlear cepstrogram (CCGRAM) to derive noise robust representations of input
speech, that are then further refined through a self-supervised learning
scheme. The latter employs SimCLR to generate contrastive views of a CCGRAM
through masking of its angle and quefrency dimensions. Our experimental
approach and validations on the emotion recognition K-EmoCon benchmark dataset,
for the first time via a speaker-independent approach, features unsupervised
pre-training, linear probing and fine-tuning. Our results potentiate
CochCeps-Augment to serve as a standard tool in speech emotion recognition
analysis, showing the added value of incorporating bio-inspired masking as an
informative augmentation task for self-supervision. Our code for implementing
CochCeps-Augment will be made available at:
https://github.com/GiannisZgs/CochCepsAugment.
