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

Zero-resource speech technology is a growing research area that aims to
develop methods for speech processing in the absence of transcriptions,
lexicons, or language modelling text. Early term discovery systems focused on
identifying isolated recurring patterns in a corpus, while more recent
full-coverage systems attempt to completely segment and cluster the audio into
word-like units---effectively performing unsupervised speech recognition. This
article presents the first attempt we are aware of to apply such a system to
large-vocabulary multi-speaker data. Our system uses a Bayesian modelling
framework with segmental word representations: each word segment is represented
as a fixed-dimensional acoustic embedding obtained by mapping the sequence of
feature frames to a single embedding vector. We compare our system on English
and Xitsonga datasets to state-of-the-art baselines, using a variety of
measures including word error rate (obtained by mapping the unsupervised output
to ground truth transcriptions). Very high word error rates are reported---in
the order of 70--80% for speaker-dependent and 80--95% for speaker-independent
systems---highlighting the difficulty of this task. Nevertheless, in terms of
cluster quality and word segmentation metrics, we show that by imposing a
consistent top-down segmentation while also using bottom-up knowledge from
detected syllable boundaries, both single-speaker and multi-speaker versions of
our system outperform a purely bottom-up single-speaker syllable-based
approach. We also show that the discovered clusters can be made less speaker-
and gender-specific by using an unsupervised autoencoder-like feature extractor
to learn better frame-level features (prior to embedding). Our system's
discovered clusters are still less pure than those of unsupervised term
discovery systems, but provide far greater coverage.

本文介绍了一种基于贝叶斯建模框架和语音嵌入向量的无监督语音识别方法，针对多个讲话者的语音数据进行了初步探索，最终通过顶层一致性分割和底层音节边界检测方法得出更好的单人和多人语音聚类结果。