  Pre-trained model representations have demonstrated state-of-the-art
performance in speech recognition, natural language processing, and other
applications. Speech models, such as Bidirectional Encoder Representations from
Transformers (BERT) and Hidden units BERT (HuBERT), have enabled generating
lexical and acoustic representations to benefit speech recognition
applications. We investigated the use of pre-trained model representations for
estimating dimensional emotions, such as activation, valence, and dominance,
from speech. We observed that while valence may rely heavily on lexical
representations, activation and dominance rely mostly on acoustic information.
In this work, we used multi-modal fusion representations from pre-trained
models to generate state-of-the-art speech emotion estimation, and we showed a
100% and 30% relative improvement in concordance correlation coefficient (CCC)
on valence estimation compared to standard acoustic and lexical baselines.
Finally, we investigated the robustness of pre-trained model representations
against noise and reverberation degradation and noticed that lexical and
acoustic representations are impacted differently. We discovered that lexical
representations are more robust to distortions compared to acoustic
representations, and demonstrated that knowledge distillation from a
multi-modal model helps to improve the noise-robustness of acoustic-based
models.
