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Found 9 papers, 1 papers with code
A multi-modal approach for identifying schizophrenia using cross-modal attention
This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms.
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables
The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs.
Speaker-independent Speech Inversion for Estimation of Nasalance
Nasalance is an objective measure derived from the oral and nasal acoustic signals that correlate with nasality.
Learning to Compute the Articulatory Representations of Speech with the MIRRORNET
Most organisms including humans function by coordinating and integrating sensory signals with motor actions to survive and accomplish desired tasks.
The Secret Source : Incorporating Source Features to Improve Acoustic-to-Articulatory Speech Inversion
The proposed SI system with the HPRC dataset gains an improvement of close to 28% when the source features are used as additional targets.
Acoustic-to-articulatory Speech Inversion with Multi-task Learning
Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data.
The Mirrornet : Learning Audio Synthesizer Controls Inspired by Sensorimotor Interaction
Experiments to understand the sensorimotor neural interactions in the human cortical speech system support the existence of a bidirectional flow of interactions between the auditory and motor regions.
Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e. g. hallucinations and delusions), using two distinct channel-delay correlation methods.
