113 citations · 156 across the 20 of their papers we have counts for
21 papers
Speaker-independent Speech Inversion for Estimation of Nasalance
Yashish M. Siriwardena, Carol Espy-Wilson, Suzanne Boyce +2
The velopharyngeal (VP) valve regulates the opening between the nasal and oral cavities. This valve opens and closes through a coordinated motion of the velum and pharyngeal walls.…
Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of /r/ in Child Speech Sound Disorders
Nina R Benway, Yashish M Siriwardena, Jonathan L Preston +3
Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispro…
Enhancing Speech Articulation Analysis using a Geometric Transformation of the X-ray Microbeam Dataset
Ahmed Adel Attia, Mark Tiede, Carol Y. Espy-Wilson
Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability…
The Secret Source : Incorporating Source Features to Improve Acoustic-to-Articulatory Speech Inversion
Yashish M. Siriwardena, Carol Espy-Wilson
In this work, we incorporated acoustically derived source features, aperiodicity, periodicity and pitch as additional targets to an acoustic-to-articulatory speech inversion (SI) s…
Learning to Compute the Articulatory Representations of Speech with the MIRRORNET
Yashish M. Siriwardena, Carol Espy-Wilson, Shihab Shamma
Most organisms including humans function by coordinating and integrating sensory signals with motor actions to survive and accomplish desired tasks. Learning these complex sensorim…
Masked Autoencoders Are Articulatory Learners
Ahmed Adel Attia, Carol Espy-Wilson
Articulatory recordings track the positions and motion of different articulators along the vocal tract and are widely used to study speech production and to develop speech technolo…
An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models
Rahil Parikh, Gaspar Rochette, Carol Espy-Wilson +1
End-to-end learning models have demonstrated a remarkable capability in performing speech segregation. Despite their wide-scope of real-world applications, little is known about th…
Acoustic-to-articulatory Speech Inversion with Multi-task Learning
Yashish M. Siriwardena, Ganesh Sivaraman, Carol Espy-Wilson
Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition. This paper…
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
Yashish M. Siriwardena, Ahmed Adel Attia, Ganesh Sivaraman +1
Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developin…
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems
Rahil Parikh, Ilya Kavalerov, Carol Espy-Wilson +1
Recent advancements in deep learning have led to drastic improvements in speech segregation models. Despite their success and growing applicability, few efforts have been made to a…
Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals
Rahil Parikh, Nadee Seneviratne, Ganesh Sivaraman +2
Multi-resolution spectro-temporal features of a speech signal represent how the brain perceives sounds by tuning cortical cells to different spectral and temporal modulations. Thes…
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings
Nadee Seneviratne, Carol Espy-Wilson
Multimodal depression classification has gained immense popularity over the recent years. We develop a multimodal depression classification system using articulatory coordination f…
Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks
Yashish M. Siriwardena, Chris Kitchen, Deanna L. Kelly +1
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g. hallucinations and delusions), using two distinct c…
Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model
Nadee Seneviratne, Carol Espy-Wilson
Speech based depression classification has gained immense popularity over the recent years. However, most of the classification studies have focused on binary classification to dis…
Inverted Vocal Tract Variables and Facial Action Units to Quantify Neuromotor Coordination in Schizophrenia
Yashish Maduwantha H. P. E. R. S, Chris Kitchen, Deanna L. Kelly +1
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g.hallucinations and delusions), using a time delay em…
Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop
Matthew Marge, Carol Espy-Wilson, Nigel Ward
With robotics rapidly advancing, more effective human-robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be pa…
Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables
Nadee Seneviratne, Carol Espy-Wilson
Depression detection using vocal biomarkers is a highly researched area. Articulatory coordination features (ACFs) are developed based on the changes in neuromotor coordination due…
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks
Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson
Emotion recognition is a classic field of research with a typical setup extracting features and feeding them through a classifier for prediction. On the other hand, generative mode…
On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson
Generative Adversarial Networks (GANs) have gained a lot of attention from machine learning community due to their ability to learn and mimic an input data distribution. GANs consi…
Semi-supervised and Transfer learning approaches for low resource sentiment classification
Rahul Gupta, Saurabh Sahu, Carol Espy-Wilson +1
Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event. Although researchers have investigated several methods to rel…
Adversarial Auto-encoders for Speech Based Emotion Recognition
Saurabh Sahu, Rahul Gupta, Ganesh Sivaraman +2
Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks su…