Speaker embeddings represent a means to extract representative vectorial
representations from a speech signal such that the representation pertains to
the speaker identity alone. The embeddings are commonly used to classify and
discriminate between different speakers. However, there is no objective measure
to evaluate the ability of a speaker embedding to disentangle the speaker
identity from the other speech characteristics. This means that the embeddings
are far from ideal, highly dependent on the training corpus and still include a
degree of residual information pertaining to factors such as linguistic
content, recording conditions or speaking style of the utterance. This paper
introduces an analysis over six sets of speaker embeddings extracted with some
of the most recent and high-performing DNN architectures, and in particular,
the degree to which they are able to truly disentangle the speaker identity
from the speech signal. To correctly evaluate the architectures, a large
multi-speaker parallel speech dataset is used. The dataset includes 46 speakers
uttering the same set of prompts, recorded in either a professional studio or
their home environments. The analysis looks into the intra- and inter-speaker
similarity measures computed over the different embedding sets, as well as if
simple classification and regression methods are able to extract several
residual information factors from the speaker embeddings. The results show that
the discriminative power of the analyzed embeddings is very high, yet across
all the analyzed architectures, residual information is still present in the
representations in the form of a high correlation to the recording conditions,
linguistic contents and utterance duration.

本文介绍了针对六个最新高性能的 DNN 体系结构提取出的演讲者嵌入的分析，重点关注它们能够从语音信号中真正区分讲话者身份的程度。结果表明，这些嵌入的区分能力非常高，但在所有已分析的体系结构中，残余信息仍呈现为与录音条件、语言内容和话语持续时间高度相关的形式。

深度说话人嵌入架构中的残差信息

Residual Information in Deep Speaker Embedding Architectures

Attention-based neural encoder-decoder frameworks have been widely used for
image captioning. Many of these frameworks deploy their full focus on
generating the caption from scratch by relying solely on the image features or
the object detection regional features. In this paper, we introduce a novel
framework that learns to modify existing captions from a given framework by
modeling the residual information, where at each timestep the model learns what
to keep, remove or add to the existing caption allowing the model to fully
focus on "what to modify" rather than on "what to predict". We evaluate our
method on the COCO dataset, trained on top of several image captioning
frameworks and show that our model successfully modifies captions yielding
better ones with better evaluation scores.

通过建模剩余信息并在每个时间步长决定保留、删除或添加已有语句，本研究提出了一种新的模型框架，能够成功地修改原有的内容从而提高评估分数。