This research addresses the challenge of developing speech applications for
zero-resource languages that lack labelled data. It specifically uses acoustic
word embedding (AWE) -- fixed-dimensional representations of variable-duration
speech segments -- employing multilingual transfer, where labelled data from
several well-resourced languages are used for pertaining. The study introduces
a new neural network that outperforms existing AWE models on zero-resource
languages. It explores the impact of the choice of well-resourced languages.
AWEs are applied to a keyword-spotting system for hate speech detection in
Swahili radio broadcasts, demonstrating robustness in real-world scenarios.
Additionally, novel semantic AWE models improve semantic query-by-example
search.

该研究针对缺乏已标注数据的零资源语言开发语音应用的挑战，特别使用声学词嵌入（AWE），通过多语言转移利用来自几种资源丰富的语言的已标注数据。该研究介绍了一种新的神经网络，优于现有的 AWE 模型在零资源语言上的表现，研究还探讨了资源丰富语言的选择对结果的影响。AWE 应用于斯瓦希里语广播中的仇恨言论检测的关键词识别系统，展示了在实际场景中的稳健性。此外，新颖的语义 AWE 模型提高了语义查询示例搜索的性能。

零资源语言的多语种音频词向量

Multilingual acoustic word embeddings for zero-resource languages

We consider hate speech detection through keyword spotting on radio
broadcasts. One approach is to build an automatic speech recognition (ASR)
system for the target low-resource language. We compare this to using acoustic
word embedding (AWE) models that map speech segments to a space where matching
words have similar vectors. We specifically use a multilingual AWE model
trained on labelled data from well-resourced languages to spot keywords in data
in the unseen target language. In contrast to ASR, the AWE approach only
requires a few keyword exemplars. In controlled experiments on Wolof and
Swahili where training and test data are from the same domain, an ASR model
trained on just five minutes of data outperforms the AWE approach. But in an
in-the-wild test on Swahili radio broadcasts with actual hate speech keywords,
the AWE model (using one minute of template data) is more robust, giving
similar performance to an ASR system trained on 30 hours of labelled data.

通过关键词检测辨别广播中的仇恨言论，使用语音识别系统和声学词嵌入模型来检测寡资源语言中的关键词，与语音识别相比，声学词嵌入方法只需要少量关键词示例，并且在野外测试中具有更好的性能。

低资源语言中的仇恨言论检测：比较 ASR 和语音词嵌入在 Wolof 和斯瓦希里语中的表现

Towards hate speech detection in low-resource languages: Comparing ASR  to acoustic word embeddings on Wolof and Swahili

Although supervised deep learning has revolutionized speech and audio
processing, it has necessitated the building of specialist models for
individual tasks and application scenarios. It is likewise difficult to apply
this to dialects and languages for which only limited labeled data is
available. Self-supervised representation learning methods promise a single
universal model that would benefit a wide variety of tasks and domains. Such
methods have shown success in natural language processing and computer vision
domains, achieving new levels of performance while reducing the number of
labels required for many downstream scenarios. Speech representation learning
is experiencing similar progress in three main categories: generative,
contrastive, and predictive methods. Other approaches rely on multi-modal data
for pre-training, mixing text or visual data streams with speech. Although
self-supervised speech representation is still a nascent research area, it is
closely related to acoustic word embedding and learning with zero lexical
resources, both of which have seen active research for many years. This review
presents approaches for self-supervised speech representation learning and
their connection to other research areas. Since many current methods focus
solely on automatic speech recognition as a downstream task, we review recent
efforts on benchmarking learned representations to extend the application
beyond speech recognition.

本论文综述了自监督语音表示学习的方法及其与其他研究领域的联系，讨论了将学习到的表示推广到语音识别以外的应用的最新研究进展。