96 citations · 149 across the 11 of their papers we have counts for
13 papers
Towards clinical adoption of voice and speech as measures of health: the need for harmonization
Nicholas Cummins, Vikram Ramanarayanan, Daniel Low +9
Speech and voice are multidimensional signals that capture both communicative intent and underlying physiological processes, providing a unique, non-invasive window into health. An…
Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking
Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia +4
Consonants contribute unequally to whether a word is understood. Given the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritiz…
Requirements for Mass Adoption of Assistive Listening Technology by the General Public
Thomas B. Kaufmann, Mehdi Foroogozar, Julie Liss +1
Assistive listening systems (ALSs) dramatically increase speech intelligibility and reduce listening effort. It is very likely that essentially everyone, not only individuals with…
Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
Jianwei Zhang, Julie Liss, Suren Jayasuriya +1
Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical intere…
TorchDIVA: An Extensible Computational Model of Speech Production built on an Open-Source Machine Learning Library
Sean Kinahan, Julie Liss, Visar Berisha
The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tr…
Consonant-Vowel Transition Models Based on Deep Learning for Objective Evaluation of Articulation
Vikram C. Mathad, Julie M. Liss, Kathy Chapman +2
Spectro-temporal dynamics of consonant-vowel (CV) transition regions are considered to provide robust cues related to articulation. In this work, we propose an objective measure of…
A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children with Cleft Palate
Vikram C. Mathad, Nancy Scherer, Kathy Chapman +2
Objectives: Evaluation of hypernasality requires extensive perceptual training by clinicians and extending this training on a large scale internationally is untenable; this compoun…
Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features
Michael Saxon, Ayush Tripathi, Yishan Jiao +2
Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies an…
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
Rohit Voleti, Julie M. Liss, Visar Berisha
It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health o…
Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder
Rohit Voleti, Stephanie Woolridge, Julie M. Liss +3
Several studies have shown that speech and language features, automatically extracted from clinical interviews or spontaneous discourse, have diagnostic value for mental disorders…
Investigating the Effects of Word Substitution Errors on Sentence Embeddings
Rohit Voleti, Julie M. Liss, Visar Berisha
A key initial step in several natural language processing (NLP) tasks involves embedding phrases of text to vectors of real numbers that preserve semantic meaning. To that end, sev…
Investigating the role of L1 in automatic pronunciation evaluation of L2 speech
Ming Tu, Anna Grabek, Julie Liss +1
Automatic pronunciation evaluation plays an important role in pronunciation training and second language education. This field draws heavily on concepts from automatic speech recog…
Simulating dysarthric speech for training data augmentation in clinical speech applications
Yishan Jiao, Ming Tu, Visar Berisha +1
Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is…