While there exist scores of natural languages, each with its unique features
and idiosyncrasies, they all share a unifying theme: enabling human
communication. We may thus reasonably predict that human cognition shapes how
these languages evolve and are used. Assuming that the capacity to process
information is roughly constant across human populations, we expect a
surprisal--duration trade-off to arise both across and within languages. We
analyse this trade-off using a corpus of 600 languages and, after controlling
for several potential confounds, we find strong supporting evidence in both
settings. Specifically, we find that, on average, phones are produced faster in
languages where they are less surprising, and vice versa. Further, we confirm
that more surprising phones are longer, on average, in 319 languages out of the
600. We thus conclude that there is strong evidence of a surprisal--duration
trade-off in operation, both across and within the world's languages.

研究发现，人类认知塑造了语言的演变和使用，600 种语言的语料库证实了超出和内部有惊讶 - 持续时间权衡的存在，结果是在不同语言中，更少惊讶的音位生成速度更快，反之亦然，并且确认了 319 种语言中更惊讶的音位平均更长。

跨语言和同一语言中的惊奇度 - 持续时间权衡

A surprisal--duration trade-off across and within the world's languages

Word segmentation, the problem of finding word boundaries in speech, is of
interest for a range of tasks. Previous papers have suggested that for
sequence-to-sequence models trained on tasks such as speech translation or
speech recognition, attention can be used to locate and segment the words. We
show, however, that even on monolingual data this approach is brittle. In our
experiments with different input types, data sizes, and segmentation
algorithms, only models trained to predict phones from words succeed in the
task. Models trained to predict words from either phones or speech (i.e., the
opposite direction needed to generalize to new data), yield much worse results,
suggesting that attention-based segmentation is only useful in limited
scenarios.

在语音领域中，基于注意力机制的序列到序列模型被用于解决诸如语音翻译或语音识别等任务中的词语分割问题。但本研究表明，仅依靠注意力机制是不稳健的，只有在训练数据包含话语标注的情况下才具有可用性。

关于使用注意力机制分隔单词的难度

On the Difficulty of Segmenting Words with Attention

Multilingual models can improve language processing, particularly for low
resource situations, by sharing parameters across languages. Multilingual
acoustic models, however, generally ignore the difference between phonemes
(sounds that can support lexical contrasts in a particular language) and their
corresponding phones (the sounds that are actually spoken, which are language
independent). This can lead to performance degradation when combining a variety
of training languages, as identically annotated phonemes can actually
correspond to several different underlying phonetic realizations. In this work,
we propose a joint model of both language-independent phone and
language-dependent phoneme distributions. In multilingual ASR experiments over
11 languages, we find that this model improves testing performance by 2%
phoneme error rate absolute in low-resource conditions. Additionally, because
we are explicitly modeling language-independent phones, we can build a
(nearly-)universal phone recognizer that, when combined with the PHOIBLE large,
manually curated database of phone inventories, can be customized into 2,000
language dependent recognizers. Experiments on two low-resourced indigenous
languages, Inuktitut and Tusom, show that our recognizer achieves phone
accuracy improvements of more than 17%, moving a step closer to speech
recognition for all languages in the world.

本研究中，通过建立联合模型来处理语言独立性的音位和语言相关性的音素的分布，改进了语音识别性能，同时可以建立一种 (几乎) 通用的音位识别器，当与 PHOIBLE 大型手动编辑的音位库相结合时，可定制为 2,000 个语言相关的识别器，此识别器可在低资源状况下实现语音识别。