Can You Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?
Self-supervised features are typically used in place of filter-bank features in speaker verification models. However, these models were originally designed to ingest filter-banks as inputs, and thus, training them on self-supervised features assumes that both feature types require the same amount of learning for the task. In this work, we observe that pre-trained self-supervised speech features inherently include information required for a downstream speaker verification task, and therefore, we can simplify the downstream model without sacrificing performance. To this end, we revisit the…
This paper was accepted at the workshop "Self-Supervised Learning - Theory and Practice" at NeurIPS 2022. Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios, the downstream task might be biased with respect to the target label distribution. This…
Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. The acquired representations from SSL have demonstrated useful for many downstream tasks including clustering, and linear classification, etc. To ensure smoothness of the representation space,…
Self-supervised learning (SSL) has made significant advances in speech representation learning. Models like wav2vec 2.0 and HuBERT have achieved state-of-the-art results in tasks such as speech recognition, particularly in monolingual settings. However, multilingual SSL models tend to underperform their monolingual counterparts on each individual language, especially in multilingual scenarios with…
Model weights: https://huggingface.co/Comfy-Org/Lens PR: https://github.com/Comfy-Org/ComfyUI/pull/14077 You'll need to git the merge pull request if you're…