Biosignal sensors and deep learning-based speech recognition: A review
Voice is one of the essential mechanisms for communicating and expressing one's
intentions as a human being. There are several causes of voice inability, including disease …
intentions as a human being. There are several causes of voice inability, including disease …
[HTML][HTML] Automatic Speech Recognition: A survey of deep learning techniques and approaches
H Ahlawat, N Aggarwal, D Gupta - International Journal of Cognitive …, 2025 - Elsevier
Significant research has been conducted during the last decade on the application of
machine learning for speech processing, particularly speech recognition. However, in recent …
machine learning for speech processing, particularly speech recognition. However, in recent …
The accented english speech recognition challenge 2020: open datasets, tracks, baselines, results and methods
The variety of accents has posed a big challenge to speech recognition. The Accented
English Speech Recognition Challenge (AESRC2020) is designed for providing a common …
English Speech Recognition Challenge (AESRC2020) is designed for providing a common …
Afrispeech-200: Pan-african accented speech dataset for clinical and general domain asr
Africa has a very poor doctor-to-patient ratio. At very busy clinics, doctors could see 30+
patients per day—a heavy patient burden compared with developed countries—but …
patients per day—a heavy patient burden compared with developed countries—but …
Mitigating bias against non-native accents
Automatic speech recognition (ASR) systems have seen substantial improvements in the
past decade; however, not for all speaker groups. Recent research shows that bias exists …
past decade; however, not for all speaker groups. Recent research shows that bias exists …
Accented speech recognition: A survey
A Hinsvark, N Delworth, M Del Rio… - arxiv preprint arxiv …, 2021 - arxiv.org
Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The
phonetic and linguistic variability of accents present hard challenges for ASR systems today …
phonetic and linguistic variability of accents present hard challenges for ASR systems today …
Losses can be blessings: Routing self-supervised speech representations towards efficient multilingual and multitask speech processing
Self-supervised learning (SSL) for rich speech representations has achieved empirical
success in low-resource Automatic Speech Recognition (ASR) and other speech processing …
success in low-resource Automatic Speech Recognition (ASR) and other speech processing …
How accents confound: Probing for accent information in end-to-end speech recognition systems
In this work, we present a detailed analysis of how accent information is reflected in the
internal representation of speech in an end-to-end automatic speech recognition (ASR) …
internal representation of speech in an end-to-end automatic speech recognition (ASR) …
Redat: Accent-invariant representation for end-to-end asr by domain adversarial training with relabeling
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address
this problem by building an accent-robust RNN-T system with domain adversarial training …
this problem by building an accent-robust RNN-T system with domain adversarial training …
Accent-robust automatic speech recognition using supervised and unsupervised wav2vec embeddings
Speech recognition models often obtain degraded performance when tested on speech with
unseen accents. Domain-adversarial training (DAT) and multi-task learning (MTL) are two …
unseen accents. Domain-adversarial training (DAT) and multi-task learning (MTL) are two …