Self-supervised models have had great success in learning speech
representations that can generalize to various downstream tasks. HuBERT, in
particular, achieves strong performance while being relatively simple in
training compared to others. The original experimental setting is
computationally extensive, hindering the reproducibility of the models. It is
also unclear why certain design decisions are made, such as the ad-hoc loss
function, and whether these decisions have an impact on the learned
representations. We propose MelHuBERT, a simplified version of HuBERT that
takes Mel spectrograms as input, significantly reducing computation and memory
consumption. We study several aspects of training, including the loss function,
multi-stage training, and streaming options. Our result is a efficient yet
performant model that can be trained on a single GPU.

该研究论文介绍了一种用于语音表示学习的自监督模型 HuBERT 以及其简化版本 MelHuBERT，可以使用 Mel 频谱作为输入训练单个 GPU 上的高效模型。研究还围绕损失函数、多阶段训练和数据流选项进行了探讨。

MelHuBERT：基于 Mel 频谱的精简 HuBERT

MelHuBERT: A simplified HuBERT on Mel spectrogram

Adaptive Computation (AC) has been shown to be effective in improving the
efficiency of Open-Domain Question Answering (ODQA) systems. However, current
AC approaches require tuning of all model parameters, and training
state-of-the-art ODQA models requires significant computational resources that
may not be available for most researchers. We propose Adaptive Passage Encoder,
an AC method that can be applied to an existing ODQA model and can be trained
efficiently on a single GPU. It keeps the parameters of the base ODQA model
fixed, but it overrides the default layer-by-layer computation of the encoder
with an AC policy that is trained to optimise the computational efficiency of
the model. Our experimental results show that our method improves upon a
state-of-the-art model on two datasets, and is also more accurate than previous
AC methods due to the stronger base ODQA model. All source code and datasets
are available at this https URL

本文介绍了一种名为 Adaptive Passage Encoder 的方法，通过进行 AC 优化，可在单个 GPU 上高效训练现有的 ODQA 模型，从而提高计算效率以及模型准确性。