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.

研究了预训练模型表示在情感语音识别中的应用，探讨了情感维度的表示方式，发现情感的激活度和支配力主要依赖于声学信息，而情感的价值主要依赖于词汇信息。使用多模态融合表示生成了最先进的情感识别，改进了情感估计中的准确度，发现词汇表示比声学表示更具有鲁棒性，并证明多模态模型知识蒸馏有助于提高基于声音的模型的噪声抗干扰能力。