The efficacy of self-supervised speech models has been validated, yet the
optimal utilization of their representations remains challenging across diverse
tasks. In this study, we delve into Acoustic Word Embeddings (AWEs), a
fixed-length feature derived from continuous representations, to explore their
advantages in specific tasks. AWEs have previously shown utility in capturing
acoustic discriminability. In light of this, we propose measuring layer-wise
similarity between AWEs and word embeddings, aiming to further investigate the
inherent context within AWEs. Moreover, we evaluate the contribution of AWEs,
in comparison to other types of speech features, in the context of Speech
Emotion Recognition (SER). Through a comparative experiment and a layer-wise
accuracy analysis on two distinct corpora, IEMOCAP and ESD, we explore
differences between AWEs and raw self-supervised representations, as well as
the proper utilization of AWEs alone and in combination with word embeddings.
Our findings underscore the acoustic context conveyed by AWEs and showcase the
highly competitive SER accuracies by appropriately employing AWEs.

研究验证了自我监督语音模型的有效性，但是在各种任务中最佳利用其表示仍然具有挑战性。本研究深入研究了声学词嵌入（AWEs），这是从连续表示中得出的固定长度特征，以探索它们在特定任务中的优势。通过在两个不同的语料库 IEMOCAP 和 ESD 上进行比较实验和层次准确性分析，我们探讨了 AWEs 和原始自我监督表示之间的差异，以及适当地使用 AWEs 与词嵌入相结合所取得的高竞争性 SER 准确性。