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

In this paper, we present a deep-learning-based framework for audio-visual
speech inpainting, i.e., the task of restoring the missing parts of an acoustic
speech signal from reliable audio context and uncorrupted visual information.
Recent work focuses solely on audio-only methods and generally aims at
inpainting music signals, which show highly different structure than speech.
Instead, we inpaint speech signals with gaps ranging from 100 ms to 1600 ms to
investigate the contribution that vision can provide for gaps of different
duration. We also experiment with a multi-task learning approach where a phone
recognition task is learned together with speech inpainting. Results show that
the performance of audio-only speech inpainting approaches degrades rapidly
when gaps get large, while the proposed audio-visual approach is able to
plausibly restore missing information. In addition, we show that multi-task
learning is effective, although the largest contribution to performance comes
from vision.

本文介绍了一种基于深度学习的框架，用于音频 - 视觉的语音修复，展示了多任务学习与视觉信息对语音信号修复的有效贡献并比较了仅音频信息下的表现。