Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Recent progress in self-training, self-supervised pretraining and unsupervised learning
enabled well performing speech recognition systems without any labeled data. However, in …

We improve low-resource ASR by integrating the ideas of multilingual training and self-
supervised learning. Concretely, we leverage an International Phonetic Alphabet (IPA) …

Millions of individuals across the world use automatic speech recognition (ASR) systems
every day to dictate messages, operate gadgets, begin searches, and enable data entry in …

The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a discrete
phonetic representation. G2P conversion is beneficial to various speech processing …

Transformers have evolved with great success in various artificial intelligence tasks. Thanks
to our recent prevalence of self-attention mechanisms, which capture long-term …

Multilingual Automatic Speech Recognition (ASR) for Indian languages is an obvious
technique for leveraging their similarities. We present a detailed analysis of how …

UniGlyph is a constructed language (conlang) designed to create a universal transliteration
system using a script derived from seven-segment characters. The goal of UniGlyph is to …

Deep neural networks perform well in image recognition, object recognition, pattern
analysis, and speech recognition. In military applications, deep neural networks can detect …

This article presents the research work on improving speech recognition systems for the
morphologically complex Malayalam language using subword tokens for language …