This study addresses the widening gap in Automatic Speech Recognition (ASR)
research between high resource and extremely low resource languages, with a
particular focus on Manchu, a critically endangered language. Manchu
exemplifies the challenges faced by marginalized linguistic communities in
accessing state-of-the-art technologies. In a pioneering effort, we introduce
the first-ever Manchu ASR model ManWav, leveraging Wav2Vec2-XLSR-53. The
results of the first Manchu ASR is promising, especially when trained with our
augmented data. Wav2Vec2-XLSR-53 fine-tuned with augmented data demonstrates a
0.02 drop in CER and 0.13 drop in WER compared to the same base model
fine-tuned with original data.

这项研究讨论了在高资源语言和极低资源语言之间自动语音识别（ASR）研究的差距扩大的问题，重点关注满洲语，这是一种濒临灭绝的语言。研究引入了第一款满洲语 ASR 模型 ManWav，利用 Wav2Vec2-XLSR-53 技术。在采用增强数据进行训练的情况下，模型的结果表明 CER 下降了 0.02，WER 下降了 0.13，相较于使用原始数据进行训练的同一基模型。

满语语音识别模型 ManWav

ManWav: The First Manchu ASR Model

We present the first neural machine translation system for translation
between the endangered Erzya language and Russian and the dataset collected by
us to train and evaluate it. The BLEU scores are 17 and 19 for translation to
Erzya and Russian respectively, and more than half of the translations are
rated as acceptable by native speakers. We also adapt our model to translate
between Erzya and 10 other languages, but without additional parallel data, the
quality on these directions remains low. We release the translation models
along with the collected text corpus, a new language identification model, and
a multilingual sentence encoder adapted for the Erzya language. These resources
will be available at this https URL.

本文介绍了第一个神经机器翻译系统，用于翻译濒危的艾尔济亚语和俄语之间的文本，BLEU 得分分别为 17 和 19，适用于艾尔济亚语和俄语之间的翻译。我们还用适应我们的模型翻译艾尔济亚语和 10 种其他语言，但在这些方向上没有额外的并行数据，质量仍然很低。我们发布了翻译模型以及收集的文本语料库、新的语言识别模型和适用于艾尔济亚语的多语句编码器。

厄尔兹亚语的第一个神经机器翻译系统

The first neural machine translation system for the Erzya language

Most work on part-of-speech (POS) tagging is focused on high resource
languages, or examines low-resource and active learning settings through
simulated studies. We evaluate POS tagging techniques on an actual endangered
language, Griko. We present a resource that contains 114 narratives in Griko,
along with sentence-level translations in Italian, and provides gold
annotations for the test set. Based on a previously collected small corpus, we
investigate several traditional methods, as well as methods that take advantage
of monolingual data or project cross-lingual POS tags. We show that the
combination of a semi-supervised method with cross-lingual transfer is more
appropriate for this extremely challenging setting, with the best tagger
achieving an accuracy of 72.9%. With an applied active learning scheme, which
we use to collect sentence-level annotations over the test set, we achieve
improvements of more than 21 percentage points.

本文研究了 POS 标注在濒危语言 Griko 上的应用，发现半监督方法和跨语言迁移方法的结合适用于这种极具挑战性的情境，并应用主动学习技术在测试集上取得了超过 21 个百分点的提升。

一个濒危语言的词性标注：Griko-Italian 平行语料资源

Part-of-Speech Tagging on an Endangered Language: a Parallel  Griko-Italian Resource

Recently proposed data collection frameworks for endangered language
documentation aim not only to collect speech in the language of interest, but
also to collect translations into a high-resource language that will render the
collected resource interpretable. We focus on this scenario and explore whether
we can improve transcription quality under these extremely low-resource
settings with the assistance of text translations. We present a neural
multi-source model and evaluate several variations of it on three low-resource
datasets. We find that our multi-source model with shared attention outperforms
the baselines, reducing transcription character error rate by up to 12.3%.

研究对濒危语言的数据采集，利用多源神经网络模型和翻译，成功提高了转录质量。