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Automatic Assessment of Dysarthria Using Audio-visual Vowel Graph Attention Network
Currently, research on the automatic assessment of dysarthria primarily focuses on two approaches: one that utilizes expert features combined with machine learning, and the other that employs data-driven deep learning methods to extract representations.
An Audio-textual Diffusion Model For Converting Speech Signals Into Ultrasound Tongue Imaging Data
In this model, the inherent acoustic characteristics of individuals related to the tongue motion details are encoded by using wav2vec 2. 0, while the ASR transcriptions related to the universality of tongue motions are encoded by using BERT.
On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition
Accurate recognition of dysarthric and elderly speech remain challenging tasks to date.
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition
The shallow stream is used to acquire traditional shallow features that is beneficial for the classification of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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