PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques

Parameter-Efficient Fine-Tuning (PEFT) is increasingly recognized as an effective method in speech processing. However, the optimal approach and the placement of PEFT methods remain inconclusive. Our study conducts extensive experiments to compare different PEFT methods and their layer-wise placement adapting Differentiable Architecture Search (DARTS). We also explore the use of ensemble learning to leverage diverse PEFT strategies. The results reveal that DARTS does not outperform the baseline approach, which involves inserting the same PEFT method into all layers of a Self-Supervised Learning (SSL) model. In contrast, an ensemble learning approach, particularly one employing majority voting, demonstrates superior performance. Our statistical evidence indicates that different PEFT methods learn in varied ways. This variation might explain why the synergistic integration of various PEFT methods through ensemble learning can harness their unique learning capabilities more effectively compared to individual layer-wise optimization.

On the Utility of Self-supervised Models for Prosody-related Tasks

Self-Supervised Learning (SSL) from speech data has produced models that have achieved remarkable performance in many tasks, and that are known to implicitly represent many aspects of information latently present in speech signals. However, relatively little is known about the suitability of such models for prosody-related tasks or the extent to which they encode prosodic information. We present a new evaluation framework, SUPERB-prosody, consisting of three prosody-related downstream tasks and two pseudo tasks. We find that 13 of the 15 SSL models outperformed the baseline on all the prosody-related tasks. We also show good performance on two pseudo tasks: prosody reconstruction and future prosody prediction. We further analyze the layerwise contributions of the SSL models. Overall we conclude that SSL speech models are highly effective for prosody-related tasks.

The Ability of Self-Supervised Speech Models for Audio Representations

Self-supervised learning (SSL) speech models have achieved unprecedented success in speech representation learning, but some questions regarding their representation ability remain unanswered. This paper addresses two of them: (1) Can SSL speech models deal with non-speech audio?; (2) Would different SSL speech models have insights into diverse aspects of audio features? To answer the two questions, we conduct extensive experiments on abundant speech and non-speech audio datasets to evaluate the representation ability of currently state-of-the-art SSL speech models, which are wav2vec 2.0 and HuBERT in this paper. These experiments are carried out during NeurIPS 2021 HEAR Challenge as a standard evaluation pipeline provided by competition officials. Results show that (1) SSL speech models could extract meaningful features of a wide range of non-speech audio, while they may also fail on certain types of datasets; (2) different SSL speech models have insights into different aspects of audio features. The two conclusions provide a foundation for the ensemble of representation models. We further propose an ensemble framework to fuse speech representation models' embeddings. Our framework outperforms state-of-the-art SSL speech/audio models and has generally superior performance on abundant datasets compared with other teams in HEAR Challenge. Our code is available at https://github.com/tony10101105/HEAR-2021-NeurIPS-Challenge -- NTU-GURA.