Automatic speech recognition (ASR) performance has improved drastically in recent years, mainly enabled by self-supervised learning (SSL) based acoustic models such as wav2vec2 and large-scale multi-lingual training like Whisper. A huge challenge still exists for low-resource languages where the availability of both audio and text is limited. This is further complicated by the presence of multiple dialects like in Indian languages. However, many Indian languages can be grouped into the same families and share the same script and grammatical structure. This is where a lot of adaptation and fine-tuning techniques can be applied to overcome the low-resource nature of the data by utilising well-resourced similar languages. In such scenarios, it is important to understand the extent to which each modality, like acoustics and text, is important in building a reliable ASR. It could be the case that an abundance of acoustic data in a language reduces the need for large text-only corpora. Or, due to the availability of various pretrained acoustic models, the vice-versa could also be true. In this proposed special session, we encourage the community to explore these ideas with the data in two low-resource Indian languages of Bengali and Bhojpuri. These approaches are not limited to Indian languages, the solutions are potentially applicable to various languages spoken around the world.

翻译：自动语音识别（ASR）的性能近年来显著提升，这主要得益于基于自监督学习（SSL）的声学模型（如wav2vec2）和大规模多语言训练（如Whisper）的进步。然而，对于音频和文本资源均受限的低资源语言，仍存在巨大挑战。印度语言中存在的多种方言更使这一问题复杂化。但许多印度语言可归为同一语系，共享相同文字与语法结构。这正是可应用大量适配与微调技术的突破口——通过利用资源丰富的相似语言来克服数据的低资源特性。在此类场景中，理解声学与文本两种模态对构建可靠ASR系统的重要性程度尤为关键。可能的情况是：某种语言中丰富的声学数据可降低对大规模纯文本语料库的需求；反之，由于各类预训练声学模型的存在，也可能呈现相反的规律。本专题研讨会鼓励学界以孟加拉语和博杰普尔语这两种低资源印度语言数据为载体，探索上述思路。这些方法不仅限于印度语言，其解决方案具有推广至全球多种语言的潜力。