An independent, automated method of decoding and transcribing oral speech is
known as automatic speech recognition (ASR). A typical ASR system extracts
feature from audio recordings or streams and run one or more algorithms to map
the features to corresponding texts. Numerous of research has been done in the
field of speech signal processing in recent years. When given adequate
resources, both conventional ASR and emerging end-to-end (E2E) speech
recognition have produced promising results. However, for low-resource
languages like Bengali, the current state of ASR lags behind, although the low
resource state does not reflect upon the fact that this language is spoken by
over 500 million people all over the world. Despite its popularity, there
aren't many diverse open-source datasets available, which makes it difficult to
conduct research on Bengali speech recognition systems. This paper is a part of
the competition named `BUET CSE Fest DL Sprint'. The purpose of this paper is
to improve the speech recognition performance of the Bengali language by
adopting speech recognition technology on the E2E structure based on the
transfer learning framework. The proposed method effectively models the Bengali
language and achieves 3.819 score in `Levenshtein Mean Distance' on the test
dataset of 7747 samples, when only 1000 samples of train dataset were used to
train.

本文旨在利用基于迁移学习框架的端到端语音识别技术，提高孟加拉语的语音识别性能，并在使用仅 1000 个训练样本进行训练的情况下，在测试数据集上实现了 3.819 的 Levenshtein Mean Distance 得分。

基于 Wav2Vec2 和迁移学习的孟加拉语自动语音识别系统

An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning

Recently, there has been an increased attention towards innovating,
enhancing, building, and deploying applications of speech signal processing for
providing assistance and relief to human mankind from the Coronavirus
(COVID-19) pandemic. Many AI with speech initiatives are taken to combat with
the present situation and also to create a safe and secure environment for the
future. This paper summarises all these efforts taken by the re-search
community towards helping the individuals and the society in the fight against
COVID-19 over the past 3-4 months using speech signal processing. We also
summarise the deep techniques used in this direction to come up with capable
solutions in a short span of time. This paper further gives an overview of the
contributions from non-speech modalities that may complement or serve as
inspiration for audio and speech analysis. In addition, we discuss our
observations with respect to solution usability, challenges, and the
significant technology achievements.

本文总结了近期研究社区使用语音信号处理技术开发应用程序来帮助应对 COVID-19 大流行的努力，包括用于解决该问题的人工智能技术，以及使用语音分析技术的深度技术，此外，还讨论了补充或为音频与语音分析提供启发的非语音形式的贡献。