In this paper we discuss an in-progress work on the development of a speech
corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and
Magahi using the field methods of linguistic data collection. The total size of
the corpus currently stands at approximately 18 hours (approx. 4-5 hours each
language) and it is transcribed and annotated with grammatical information such
as part-of-speech tags, morphological features and Universal dependency
relationships. We discuss our methodology for data collection in these
languages, most of which was done in the middle of the COVID-19 pandemic, with
one of the aims being to generate some additional income for low-income groups
speaking these languages. In the paper, we also discuss the results of the
baseline experiments for automatic speech recognition system in these
languages.

本文介绍我们正在进行的工作，使用语言学数据收集的领域方法，为四种资源匮乏的印度 - 雅利安语言 —— 阿瓦德语、博杰普尔语、布拉杰语和马加希语开发语音语料库。我们还讨论了这些语言的数据收集方法，大部分是在 COVID-19 疫情期间进行的，目的之一是为讲这些语言的低收入群体提供额外的收入来源，并讨论了这些语言的自动语音识别系统的基线实验结果。

印度低资源语言的带注释语音语料库：Awadhi、Bhojpuri、Braj 和 Magahi

Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi

Hate speech is considered to be one of the major issues currently plaguing
online social media. Repeated and repetitive exposure to hate speech has been
shown to create physiological effects on the target users. Thus, hate speech,
in all its forms, should be addressed on these platforms in order to maintain
good health. In this paper, we explored several Transformer based machine
learning models for the detection of hate speech and offensive content in
English and Indo-Aryan languages at FIRE 2021. We explore several models such
as mBERT, XLMR-large, XLMR-base by team name "Super Mario". Our models came 2nd
position in Code-Mixed Data set (Macro F1: 0.7107), 2nd position in Hindi
two-class classification(Macro F1: 0.7797), 4th in English four-class category
(Macro F1: 0.8006) and 12th in English two-class category (Macro F1: 0.6447).

本篇论文探索了基于 Transformer 的多种机器学习模型，用于探测英语和印度 - 雅利安语中的仇恨言论和冒犯性内容，研究团队 “超级马里奥” 采用 mBERT、XLMR-large、XLMR-base 等多种模型，我们在 Code-Mixed 数据集排名第二（宏平均 F1：0.7107）、在印地语二分类中排名第二（宏平均 F1：0.7797）、在英语四分类中排名第四（宏平均 F1：0.8006），在英语二分类中排名第十二（宏平均 F1：0.6447）。

探索基于 Transformer 的模型以识别英语和印度雅利安语中的仇恨言论和攻击性内容

Exploring Transformer Based Models to Identify Hate Speech and Offensive  Content in English and Indo-Aryan Languages

In this paper, we discuss an attempt to develop an automatic language
identification system for 5 closely-related Indo-Aryan languages of India,
Awadhi, Bhojpuri, Braj, Hindi and Magahi. We have compiled a comparable corpora
of varying length for these languages from various resources. We discuss the
method of creation of these corpora in detail. Using these corpora, a language
identification system was developed, which currently gives state of the art
accuracy of 96.48\%. We also used these corpora to study the similarity between
the 5 languages at the lexical level, which is the first data-based study of
the extent of closeness of these languages.

本文讨论了一个自动语言识别系统的尝试，该系统用于印度 5 种密切相关的印度雅利安语言：阿瓦德语、博杰普尔语、布拉吉语、印地语和马加耳语。使用不同来源的可比较语料库，编译了这些语言的长度不同的语料库。使用这些语料库，开发了一个语言识别系统，目前的准确率为 96.48％。我们还使用这些语料库研究了 5 种语言在词汇水平上的相似性，这是这些语言亲和度首个基于数据的研究。