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Adaptation algorithms for neural network-based speech recognition: An overview
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 …
recognition, considering both hybrid hidden Markov model/neural network systems and end …
A review on speech processing using machine learning paradigm
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 …
last decade has bought gigantic evolution based on machine learning prototype. Speech …
Voice disorder classification using speech enhancement and deep learning models
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 …
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
Despite the rapid progress of automatic speech recognition (ASR) technologies targeting
normal speech in recent decades, accurate recognition of dysarthric and elderly speech …
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
Silent speech recognition (SSR) converts nonaudio information such as articulatory
movements into text. SSR has the potential to enable persons with laryngectomy to …
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 …
However, collecting dysarthric speech is difficult. The machine learning models cannot be …
On the impact of dysarthric speech on contemporary ASR cloud platforms
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 …
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 …
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 …
communicate with others. Automatic speech recognition (ASR) is a powerful tool for …
[PDF][PDF] Dysarthric Speech Recognition Using Convolutional LSTM Neural Network.
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 …
Speech in patients with dysarthria is generally characterized by poor articulation, breathy …