Most speech self-supervised learning (SSL) models are trained with a pretext
task which consists in predicting missing parts of the input signal, either
future segments (causal prediction) or segments masked anywhere within the
input (non-causal prediction). Learned speech representations can then be
efficiently transferred to downstream tasks (e.g., automatic speech or speaker
recognition). In the present study, we investigate the use of a speech SSL
model for speech inpainting, that is reconstructing a missing portion of a
speech signal from its surrounding context, i.e., fulfilling a downstream task
that is very similar to the pretext task. To that purpose, we combine an SSL
encoder, namely HuBERT, with a neural vocoder, namely HiFiGAN, playing the role
of a decoder. In particular, we propose two solutions to match the HuBERT
output with the HiFiGAN input, by freezing one and fine-tuning the other, and
vice versa. Performance of both approaches was assessed in single- and
multi-speaker settings, for both informed and blind inpainting configurations
(i.e., the position of the mask is known or unknown, respectively), with
different objective metrics and a perceptual evaluation. Performances show that
if both solutions allow to correctly reconstruct signal portions up to the size
of 200ms (and even 400ms in some cases), fine-tuning the SSL encoder provides a
more accurate signal reconstruction in the single-speaker setting case, while
freezing it (and training the neural vocoder instead) is a better strategy when
dealing with multi-speaker data.

使用语音自监督学习模型进行语音修复任务时，选择冻结一个模型并微调另一个模型以更好地适应单一或多说话者数据，提供了有效的信号重建策略。

填补空缺！结合自监督表示学习与神经声音合成实现语音修复

Fill in the Gap! Combining Self-supervised Representation Learning with  Neural Audio Synthesis for Speech Inpainting

Large-scale speech self-supervised learning (SSL) has emerged to the main
field of speech processing, however, the problem of computational cost arising
from its vast size makes a high entry barrier to academia. In addition,
existing distillation techniques of speech SSL models compress the model by
reducing layers, which induces performance degradation in linguistic pattern
recognition tasks such as phoneme recognition (PR). In this paper, we propose
FitHuBERT, which makes thinner in dimension throughout almost all model
components and deeper in layer compared to prior speech SSL distillation works.
Moreover, we employ a time-reduction layer to speed up inference time and
propose a method of hint-based distillation for less performance degradation.
Our method reduces the model to 23.8% in size and 35.9% in inference time
compared to HuBERT. Also, we achieve 12.1% word error rate and 13.3% phoneme
error rate on the SUPERB benchmark which is superior than prior work.

本文提出了一种名为 FitHuBERT 的新型语音自监督学习方法，通过在几乎所有模型组件上缩小尺寸并增加详细层级，并通过提示辅助蒸馏方法减少性能退化，将模型压缩至 HuBERT 大小的 23.8％和推理时间的 35.9％，在超级基准测试中实现 12.1％的字错误率和 13.3％的音素误差率。