The Automatic Speech Recognition (ASR) has come up with significant advancements over the course of several decades, transitioning from a rule-based method to a statistical approach, and ultimately to the use of end-to-end (E2E) frameworks. This phenomenon continues with the progression of machine learning and deep learning methodologies. The E2E approach for ASR has demonstrated predominant success in the case of resourceful languages with larger annotated corpus. However, the accuracy is quite low for low-resourced languages such as Nepali. In this regard, language-specific tools such as tokenizers seem to play a vital role in improving the performance of the E2E model for low-resourced languages like Nepali. In this paper, we propose a pronunciationaware syllable tokenizer for the Nepali language which improves the results of the E2E model. Our experiment confirm that the introduction of the proposed tokenizer yields better performance with the Character Error Rate (CER) 8.09% compared to other language-independent tokenizers.

Fine tuning of the pre-trained language model is a technique which can be used to enhance the technologies of low-resourced languages. The unsupervised approach can fine-tune any pre-trained model with minimum or even no language-specific resources. It is highly advantageous, particularly for languages that possess limited computational resources. We present a novel approach for fine-tuning a pre-trained Automatic Speech Recognition (ASR) model that is suitable for low resource languages. Our methods involves iterative fine-tuning of pre-trained ASR model. mms-1b is selected as the pretrained seed model for fine-tuning. We take the Nepali language as a case study for this research work. Our approach achieved a CER of 6.77%, outperforming all previously recorded CER values for the Nepali ASR Systems.

In this paper, we publicly release an annotated corpus of 42 decisions of the European Court of Human Rights (ECHR). The corpus is annotated in terms of three types of clauses useful in argument mining: premise, conclusion, and non-argument parts of the text. Furthermore, relationships among the premises and conclusions are mapped. We present baselines for three tasks that lead from unstructured texts to structured arguments. The tasks are argument clause recognition, clause relation prediction, and premise/conclusion recognition. Despite a straightforward application of the bidirectional encoders from Transformers (BERT), we obtained very promising results F1 0.765 on argument recognition, 0.511 on relation prediction, and 0.859/0.628 on premise/conclusion recognition). The results suggest the usefulness of pre-trained language models based on deep neural network architectures in argument mining. Because of the simplicity of the baselines, there is ample space for improvement in future work based on the released corpus.