This paper investigates the challenges in building Swiss German speech
translation systems, specifically focusing on the impact of dialect diversity
and differences between Swiss German and Standard German. Swiss German is a
spoken language with no formal writing system, it comprises many diverse
dialects and is a low-resource language with only around 5 million speakers.
The study is guided by two key research questions: how does the inclusion and
exclusion of dialects during the training of speech translation models for
Swiss German impact the performance on specific dialects, and how do the
differences between Swiss German and Standard German impact the performance of
the systems? We show that dialect diversity and linguistic differences pose
significant challenges to Swiss German speech translation, which is in line
with linguistic hypotheses derived from empirical investigations.

本研究调查了建立瑞士德语语音翻译系统的挑战，特别关注方言多样性和瑞士德语与标准德语之间的差异的影响。研究发现方言多样性和语言差异对瑞士德语语音翻译构成重大挑战，与实证研究得出的语言学假设相符。

瑞士德语语音翻译的方言迁移

Dialect Transfer for Swiss German Speech Translation

Generating spoken word embeddings that possess semantic information is a
fascinating topic. Compared with text-based embeddings, they cover both
phonetic and semantic characteristics, which can provide richer information and
are potentially helpful for improving ASR and speech translation systems. In
this paper, we review and examine the authenticity of a seminal work in this
field: Speech2Vec. First, a homophone-based inspection method is proposed to
check the speech embeddings released by the author of Speech2Vec. There is no
indication that these embeddings are generated by the Speech2Vec model.
Moreover, through further analysis of the vocabulary composition, we suspect
that a text-based model fabricates these embeddings. Finally, we reproduce the
Speech2Vec model, referring to the official code and optimal settings in the
original paper. Experiments showed that this model failed to learn effective
semantic embeddings. In word similarity benchmarks, it gets a correlation score
of 0.08 in MEN and 0.15 in WS-353-SIM tests, which is over 0.5 lower than those
described in the original paper. Our data and code are available.

本文检验了 Speech2Vec 模型的效果，发现其未能学习到有效的语义嵌入，并怀疑原文中提到的语音词向量是由一个基于文本的模型生成的。