Self-supervised learning (SSL) speech models, which can serve as powerful
upstream models to extract meaningful speech representations, have achieved
unprecedented success in speech representation learning. However, their
effectiveness on non-speech datasets is relatively less explored. In this work,
we propose an ensemble framework, with a combination of ensemble techniques, to
fuse SSL speech models' embeddings. Extensive experiments on speech and
non-speech audio datasets are conducted to investigate the representation
abilities of our ensemble method and its single constituent model. Ablation
studies are carried out to evaluate the performances of different ensemble
techniques, such as feature averaging and concatenation. All experiments are
conducted during NeurIPS 2021 HEAR Challenge as a standard evaluation pipeline
provided by competition officials. Results demonstrate SSL speech models'
strong abilities on various non-speech tasks, while we also note that they fail
to deal with fine-grained music tasks, such as pitch classification and note
onset detection. In addition, feature ensemble is shown to have great potential
on producing more holistic representations, as our proposed framework generally
surpasses state-of-the-art SSL speech/audio models and has superior performance
on various datasets compared with other teams in HEAR Challenge. Our code is
available at this https URL --
NTU-GURA.

本研究提出融合自监督学习语音模型嵌入的集成框架，旨在探究其在音频和非语音任务中的表示能力，实验证明该框架普遍优于当前最先进的自监督学习语音 / 音频模型，特别在面对细粒度音乐任务时也表现出强大的能力。