Adaptation algorithms for neural network-based speech recognition: An overview

P Bell, J Fainberg, O Klejch, J Li… - IEEE Open Journal …, 2020‏ - ieeexplore.ieee.org
We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

A review on speech processing using machine learning paradigm

KB Bhangale, K Mohanaprasad - International Journal of Speech …, 2021‏ - Springer
Speech processing plays a crucial role in many signal processing applications, while the
last decade has bought gigantic evolution based on machine learning prototype. Speech …

Voice disorder classification using speech enhancement and deep learning models

M Chaiani, SA Selouani, M Boudraa… - Biocybernetics and …, 2022‏ - Elsevier
With the recent development of speech-enabled interactive systems using artificial agents,
there has been substantial interest in the analysis and classification of voice disorders to …

Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition

M Geng, X **e, Z Ye, T Wang, G Li, S Hu… - … on Audio, Speech …, 2022‏ - ieeexplore.ieee.org
Despite the rapid progress of automatic speech recognition (ASR) technologies targeting
normal speech in recent decades, accurate recognition of dysarthric and elderly speech …

Speaker-independent silent speech recognition from flesh-point articulatory movements using an LSTM neural network

M Kim, B Cao, T Mau, J Wang - IEEE/ACM transactions on …, 2017‏ - ieeexplore.ieee.org
Silent speech recognition (SSR) converts nonaudio information such as articulatory
movements into text. SSR has the potential to enable persons with laryngectomy to …

Multi-stage audio-visual fusion for dysarthric speech recognition with pre-trained models

C Yu, X Su, Z Qian - IEEE Transactions on Neural Systems and …, 2023‏ - ieeexplore.ieee.org
Dysarthric speech recognition helps speakers with dysarthria to enjoy better communication.
However, collecting dysarthric speech is difficult. The machine learning models cannot be …

On the impact of dysarthric speech on contemporary ASR cloud platforms

L De Russis, F Corno - Journal of Reliable Intelligent Environments, 2019‏ - Springer
The spread of voice-driven devices has a positive impact for people with disabilities in smart
environments, since such devices allow them to perform a series of daily activities that were …

[HTML][HTML] A survey of automatic speech recognition for dysarthric speech

Z Qian, K **ao - Electronics, 2023‏ - mdpi.com
Dysarthric speech has several pathological characteristics, such as discontinuous
pronunciation, uncontrolled volume, slow speech, explosive pronunciation, improper …

A survey of technologies for automatic Dysarthric speech recognition

Z Qian, K **ao, C Yu - EURASIP Journal on Audio, Speech, and Music …, 2023‏ - Springer
Speakers with dysarthria often struggle to accurately pronounce words and effectively
communicate with others. Automatic speech recognition (ASR) is a powerful tool for …

[PDF][PDF] Dysarthric Speech Recognition Using Convolutional LSTM Neural Network.

MJ Kim, B Cao, K An, J Wang - Interspeech, 2018‏ - isca-archive.org
Dysarthria is a motor speech disorder that impedes the physical production of speech.
Speech in patients with dysarthria is generally characterized by poor articulation, breathy …