Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
Recent progress in the CUHK dysarthric speech recognition system
Despite the rapid progress of automatic speech recognition (ASR) technologies in the past
few decades, recognition of disordered speech remains a highly challenging task to date …
few decades, recognition of disordered speech remains a highly challenging task to date …
An empirical survey of data augmentation for time series classification with neural networks
In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …
recognition. Part of this success can be attributed to the reliance on big data to increase …
Speech vision: An end-to-end deep learning-based dysarthric automatic speech recognition system
SR Shahamiri - IEEE Transactions on Neural Systems and …, 2021 - ieeexplore.ieee.org
Dysarthria is a disorder that affects an individual's speech intelligibility due to the paralysis of
muscles and organs involved in the articulation process. As the condition is often associated …
muscles and organs involved in the articulation process. As the condition is often associated …
Residual neural network precisely quantifies dysarthria severity-level based on short-duration speech segments
Recently, we have witnessed Deep Learning methodologies gaining significant attention for
severity-based classification of dysarthric speech. Detecting dysarthria, quantifying its …
severity-based classification of dysarthric speech. Detecting dysarthria, quantifying its …
Investigation of data augmentation techniques for disordered speech recognition
Disordered speech recognition is a highly challenging task. The underlying neuro-motor
conditions of people with speech disorders, often compounded with co-occurring physical …
conditions of people with speech disorders, often compounded with co-occurring physical …
Source domain data selection for improved transfer learning targeting dysarthric speech recognition
This paper presents an improved transfer learning framework applied to robust personalised
speech recognition models for speakers with dysarthria. As the baseline of transfer learning …
speech recognition models for speakers with dysarthria. As the baseline of transfer learning …
Glottal source information for pathological voice detection
NP Narendra, P Alku - IEEE Access, 2020 - ieeexplore.ieee.org
Automatic methods for the detection of pathological voice from healthy speech can be
considered as potential clinical tools for medical treatment. This study investigates the …
considered as potential clinical tools for medical treatment. This study investigates the …
Adversarial data augmentation for disordered speech recognition
Automatic recognition of disordered speech remains a highly challenging task to date. The
underlying neuro-motor conditions, often compounded with co-occurring physical …
underlying neuro-motor conditions, often compounded with co-occurring physical …
Speaker adaptation for Wav2vec2 based dysarthric ASR
Dysarthric speech recognition has posed major challenges due to lack of training data and
heavy mismatch in speaker characteristics. Recent ASR systems have benefited from readily …
heavy mismatch in speaker characteristics. Recent ASR systems have benefited from readily …