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

Automatic Speech Recognition (ASR) systems often struggle with transcribing child speech
due to the lack of large child speech datasets required to accurately train child-friendly ASR …

Despite recent advancements in deep learning technologies, Child Speech Recognition
remains a challenging task. Current Automatic Speech Recognition (ASR) models require …

Automatic Speech Recognition (ASR) systems have progressed significantly in their
performance on adult speech data; however, transcribing child speech remains challenging …

Modern end-to-end Automatic Speech Recognition (ASR) systems struggle to recognise
children's speech. This challenge is due to the high acoustic variability in children's voices …

We compare two distinct speech recognition approaches, namely Hidden Markov models
mixed with deep neural networks and modern end-to-end neural speech recognition …

Abstract Automatic Speech Recognition (ASR) encounters unique challenges when dealing
with children's speech, mainly due to the scarcity of available data. Training large ASR …

Abstract Children's Automatic Speech Recognition (ASR) represents a considerable
challenge, with a considerable performance decline of state-of-the-art systems when …

The high variability in acoustic, pronunciation, and linguistic characteristics of children's
speech makes of children's automatic speech recognition (ASR) a complex task. Training a …

Automatic Speech Recognition (ASR) for adults' speeches has made significant progress by
employing deep neural network (DNN) models recently, but improvement in children's …