# A Comprehensive Study of the Current State-of-the-Art in Nepali Automatic Speech Recognition Systems

arXiv: https://arxiv.org/abs/2402.03050 · HTML: https://mlsanity.com/paper/2402.03050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Transfer Learning for Speech Recognition on a Budget](https://mlsanity.com/paper/1706.00290/) (2017)
- [Transfer learning of language-independent end-to-end ASR with language model fusion](https://mlsanity.com/paper/1811.02134/) (2018)

## Abstract (arXiv)

In this paper, we examine the research conducted in the field of Nepali Automatic Speech Recognition (ASR). The primary objective of this survey is to conduct a comprehensive review of the works on Nepali Automatic Speech Recognition Systems completed to date, explore the different datasets used, examine the technology utilized, and take account of the obstacles encountered in implementing the Nepali ASR system. In tandem with the global trends of ever-increasing research on speech recognition based research, the number of Nepalese ASR-related projects are also growing. Nevertheless, the investigation of language and acoustic models of the Nepali language has not received adequate attention compared to languages that possess ample resources. In this context, we provide a framework as well as directions for future investigations.