With rapid globalization, the need to build inclusive and representative
speech technology cannot be overstated. Accent is an important aspect of speech
that needs to be taken into consideration while building inclusive speech
synthesizers. Inclusive speech technology aims to erase any biases towards
specific groups, such as people of certain accent. We note that
state-of-the-art Text-to-Speech (TTS) systems may currently not be suitable for
all people, regardless of their background, as they are designed to generate
high-quality voices without focusing on accent. In this paper, we propose a TTS
model that utilizes a Multi-Level Variational Autoencoder with adversarial
learning to address accented speech synthesis and conversion in TTS, with a
vision for more inclusive systems in the future. We evaluate the performance
through both objective metrics and subjective listening tests. The results show
an improvement in accent conversion ability compared to the baseline.

提出了一种利用多级变分自编码器与对抗学习的文本转语音模型，用于解决语音合成和转换中的口音问题，以期构建更具包容性的系统。通过客观评价指标和主观听觉测试对性能进行评估，结果表明与基线相比，口音转换能力有所提高。

利用多层次 VAE 和对抗训练进行的文本转语音口音转化

Accent Conversion in Text-To-Speech Using Multi-Level VAE and  Adversarial Training

Methods that can generate synthetic speech which is perceptually
indistinguishable from speech recorded by a human speaker, are easily
available. Several incidents report misuse of synthetic speech generated from
these methods to commit fraud. To counter such misuse, many methods have been
proposed to detect synthetic speech. Some of these detectors are more
interpretable, can generalize to detect synthetic speech in the wild and are
robust to noise. However, limited work has been done on understanding bias in
these detectors. In this work, we examine bias in existing synthetic speech
detectors to determine if they will unfairly target a particular gender, age
and accent group. We also inspect whether these detectors will have a higher
misclassification rate for bona fide speech from speech-impaired speakers w.r.t
fluent speakers. Extensive experiments on 6 existing synthetic speech detectors
using more than 0.9 million speech signals demonstrate that most detectors are
gender, age and accent biased, and future work is needed to ensure fairness. To
support future research, we release our evaluation dataset, models used in our
study and source code at this https URL

现有的合成语音检测器存在性别、年龄和口音偏见，需要进一步研究以确保公正性。

公平 SSD: 认知合成语音检测器中的偏见

FairSSD: Understanding Bias in Synthetic Speech Detectors

Spoken languages show significant variation across mandarin and accent.
Despite the high performance of mandarin automatic speech recognition (ASR),
accent ASR is still a challenge task. In this paper, we introduce meta-learning
techniques for fast accent domain expansion in mandarin speech recognition,
which expands the field of accents without deteriorating the performance of
mandarin ASR. Meta-learning or learn-to-learn can learn general relation in
multi domains not only for over-fitting a specific domain. So we select
meta-learning in the domain expansion task. This more essential learning will
cause improved performance on accent domain extension tasks. We combine the
methods of meta learning and freeze of model parameters, which makes the
recognition performance more stable in different cases and the training faster
about 20%. Our approach significantly outperforms other methods about 3%
relatively in the accent domain expansion task. Compared to the baseline model,
it improves relatively 37% under the condition that the mandarin test set
remains unchanged. In addition, it also proved this method to be effective on a
large amount of data with a relative performance improvement of 4% on the
accent test set.

通过元学习技术，本研究提出了一种快速扩展普通话语音识别中口音领域的方法，有效地改善了普通话语音识别在口音方面的性能，比其他方法相对提高了约 3%。

一种用于普通话语音识别中快速扩充口音领域的元学习方案

A meta learning scheme for fast accent domain expansion in Mandarin  speech recognition

Understanding why specific items are recommended to users can significantly
increase their trust and satisfaction in the system. While neural recommenders
have become the state-of-the-art in recent years, the complexity of deep models
still makes the generation of tangible explanations for end users a challenging
problem. Existing methods are usually based on attention distributions over a
variety of features, which are still questionable regarding their suitability
as explanations, and rather unwieldy to grasp for an end user. Counterfactual
explanations based on a small set of the user's own actions have been shown to
be an acceptable solution to the tangibility problem. However, current work on
such counterfactuals cannot be readily applied to neural models. In this work,
we propose ACCENT, the first general framework for finding counterfactual
explanations for neural recommenders. It extends recently-proposed influence
functions for identifying training points most relevant to a recommendation,
from a single to a pair of items, while deducing a counterfactual set in an
iterative process. We use ACCENT to generate counterfactual explanations for
two popular neural models, Neural Collaborative Filtering (NCF) and Relational
Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of
the popular MovieLens 100K dataset.

本文提出了 ACCENT，一个基于影响函数的通用框架，用于找到深度神经网络推荐系统的因果解释。我们利用 ACCENT 生成了针对两种流行的神经网络模型（NCF 和 RCF）的因果解释，并在 MovieLens100K 数据集的样本上表明其可行性。