[PDF][PDF] Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs.
Raw waveform acoustic modelling has recently received increasing attention. Compared
with the task-blind hand-crafted features which may discard useful information …
with the task-blind hand-crafted features which may discard useful information …
ORG-RGRU: An automated diagnosed model for multiple diseases by heuristically based optimized deep learning using speech/voice signal
PVLN Rao, S Meher - Biomedical Signal Processing and Control, 2024 - Elsevier
Human ailments create an impact in altering the significant metabolism activities of the body
irrespective of various parts. Dysarthric speech is commonly known as Parkinson's disease …
irrespective of various parts. Dysarthric speech is commonly known as Parkinson's disease …
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches
Although the UA-Speech and TORGO databases of control and dysarthric speech are
invaluable resources made available to the research community with the objective of …
invaluable resources made available to the research community with the objective of …
Automatic GRBAS scoring of pathological voices using deep learning and a small set of labeled voice data
S Hidaka, Y Lee, M Nakanishi, K Wakamiya… - Journal of Voice, 2022 - Elsevier
Objectives Auditory-perceptual evaluation frameworks, such as the grade-roughness-
breathiness-asthenia-strain (GRBAS) scale, are the gold standard for the quantitative …
breathiness-asthenia-strain (GRBAS) scale, are the gold standard for the quantitative …
Speaker Embeddings as Individuality Proxy for Voice Stress Detection
Since the mental states of the speaker modulate speech, stress introduced by cognitive or
physical loads could be detected in the voice. The existing voice stress detection benchmark …
physical loads could be detected in the voice. The existing voice stress detection benchmark …
[PDF][PDF] Adversarial-Free Speaker Identity-Invariant Representation Learning for Automatic Dysarthric Speech Classification.
P Janbakhshi, I Kodrasi - INTERSPEECH, 2022 - isca-archive.org
Speech representations which are robust to pathology-unrelated cues such as speaker
identity information have been shown to be advantageous for automatic dysarthric speech …
identity information have been shown to be advantageous for automatic dysarthric speech …
E‐Speech: Development of a Dataset for Speech Emotion Recognition and Analysis
W Liu, J Shi, S Zhang, L Zhou… - International Journal of …, 2024 - Wiley Online Library
Speech emotion recognition plays a crucial role in analyzing psychological disorders,
behavioral decision‐making, and human‐machine interaction applications. However, the …
behavioral decision‐making, and human‐machine interaction applications. However, the …
[PDF][PDF] King's Research Portal
In this paper, we explore the effectiveness of deploying the raw phase and magnitude
spectra for dysarthric speech recognition, detection and classification. In particular, we …
spectra for dysarthric speech recognition, detection and classification. In particular, we …
[PDF][PDF] Test-time adaptation for automatic pathological speech detection in noisy environments
Deep learning-based pathological speech detection approaches are gaining popularity as a
diagnostic tool to support time-consuming and subjective clinical assessments. While these …
diagnostic tool to support time-consuming and subjective clinical assessments. While these …
[PDF][PDF] Adversarial Robustness Analysis in Automatic Pathological Speech Detection Approaches
Automatic pathological speech detection relies on deep learning (DL), showing promising
performance for various pathologies. Despite the critical importance of robustness in …
performance for various pathologies. Despite the critical importance of robustness in …