With the advent of general-purpose speech representations from large-scale
self-supervised models, applying a single model to multiple downstream tasks is
becoming a de-facto approach. However, the pooling problem remains; the length
of speech representations is inherently variable. The naive average pooling is
often used, even though it ignores the characteristics of speech, such as
differently lengthed phonemes. Hence, we design a novel pooling method to
squash acoustically similar representations via vector quantization, which does
not require additional training, unlike attention-based pooling. Further, we
evaluate various unsupervised pooling methods on various self-supervised
models. We gather diverse methods scattered around speech and text to evaluate
on various tasks: keyword spotting, speaker identification, intent
classification, and emotion recognition. Finally, we quantitatively and
qualitatively analyze our method, comparing it with supervised pooling methods.

本文介绍了一种新的汇集方法，通过向量量化可以将声学表示压缩为具有相似声学特性的向量，从而通过对各种下游任务进行评估，比较了我们的方法和监督汇集方法。