Adversarial learning baselines for domain adaptation (DA) approaches in the
context of semantic segmentation are under explored in semi-supervised
framework. These baselines involve solely the available labeled target samples
in the supervision loss. In this work, we propose to enhance their usefulness
on both semantic segmentation and the single domain classifier neural networks.
We design new training objective losses for cases when labeled target data
behave as source samples or as real target samples. The underlying rationale is
that considering the set of labeled target samples as part of source domain
helps reducing the domain discrepancy and, hence, improves the contribution of
the adversarial loss. To support our approach, we consider a complementary
method that mixes source and labeled target data, then applies the same
adaptation process. We further propose an unsupervised selection procedure
using entropy to optimize the choice of labeled target samples for adaptation.
We illustrate our findings through extensive experiments on the benchmarks
GTA5, SYNTHIA, and Cityscapes. The empirical evaluation highlights competitive
performance of our proposed approach.

该研究论文提出了一种在半监督框架下增强领域适应中对抗学习方法基准的方法，该方法不仅在语义分割和单一域分类器神经网络上具有实用价值，还通过设计新的训练目标损失函数来改善有标签的目标数据表现为源数据或真实目标数据的情况，并提出一种基于熵的无监督选择过程来优化适应中有标签目标样本的选择。实验证明了该方法的竞争性性能。

语义分割的对抗性半监督域自适应：标记目标样本的新角色

Adversarial Semi-Supervised Domain Adaptation for Semantic Segmentation:  A New Role for Labeled Target Samples

Connectionist Temporal Classification (CTC) models are popular for their
balance between speed and performance for Automatic Speech Recognition (ASR).
However, these CTC models still struggle in other areas, such as
personalization towards custom words. A recent approach explores Contextual
Adapters, wherein an attention-based biasing model for CTC is used to improve
the recognition of custom entities. While this approach works well with enough
data, we showcase that it isn't an effective strategy for low-resource
languages. In this work, we propose a supervision loss for smoother training of
the Contextual Adapters. Further, we explore a multilingual strategy to improve
performance with limited training data. Our method achieves 48% F1 improvement
in retrieving unseen custom entities for a low-resource language.
Interestingly, as a by-product of training the Contextual Adapters, we see a
5-11% Word Error Rate (WER) reduction in the performance of the base CTC model
as well.

本文提出了一种监督损失的方法来平滑 Contextual Adapters 的训练，进一步探讨了多语言策略以提高有限的训练数据的性能，结果在一个低资源语言中检索未见过的自定义实体方面取得了 48％ F1 的提升。

多语言上下文适配器：提高低资源语言中自定义单词识别的效果

Multilingual Contextual Adapters To Improve Custom Word Recognition In  Low-resource Languages

Adaptive text to speech (TTS) can synthesize new voices in zero-shot
scenarios efficiently, by using a well-trained source TTS model without
adapting it on the speech data of new speakers. Considering seen and unseen
speakers have diverse characteristics, zero-shot adaptive TTS requires strong
generalization ability on speaker characteristics, which brings modeling
challenges. In this paper, we develop AdaSpeech 4, a zero-shot adaptive TTS
system for high-quality speech synthesis. We model the speaker characteristics
systematically to improve the generalization on new speakers. Generally, the
modeling of speaker characteristics can be categorized into three steps:
extracting speaker representation, taking this speaker representation as
condition, and synthesizing speech/mel-spectrogram given this speaker
representation. Accordingly, we improve the modeling in three steps: 1) To
extract speaker representation with better generalization, we factorize the
speaker characteristics into basis vectors and extract speaker representation
by weighted combining of these basis vectors through attention. 2) We leverage
conditional layer normalization to integrate the extracted speaker
representation to TTS model. 3) We propose a novel supervision loss based on
the distribution of basis vectors to maintain the corresponding speaker
characteristics in generated mel-spectrograms. Without any fine-tuning,
AdaSpeech 4 achieves better voice quality and similarity than baselines in
multiple datasets.

本文提出了一个高质量的语音合成系统 AdaSpeech 4，用于零样本适应性文本到语音。该系统通过将说话者特征系统地建模来提高对新说话者的泛化能力，并通过三个步骤改善了建模。除了利用条件层归一化将提取的说话人特征整合到 TTS 模型中之外，本文基于基向量的分布提出了一种新的监督损失，以在生成的 Mel 声谱图中保持相应的说话人特征。在多个数据集中，AdaSpeech 4 实现了比基线更好的语音质量和相似性，而无需进行任何微调。