Medicine and Health Sciences Commons^™

Articulatory Distinctiveness Of Vowels And Consonants: A Data-Driven Approach, Jun Wang, Jordan R. Green, Ashok Samal, Yana Yunusova

School of Computing: Faculty Publications

Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach.

Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were collected. First, classification accuracies were obtained using 2 complementary approaches: (a) Procrustes analysis and (b) a support vector machine. Procrustes distance was then used to measure the articulatory distinctiveness among vowels and consonants. Finally, the distance (distinctiveness) matrices of different vowel pairs and consonant pairs were used to derive articulatory vowel and consonant spaces …

Word Recognition From Continuous Articulatory Movement Time-Series Data Using Symbolic Representations, Jun Wang, Arvind Balasubramanian, Luis Mojica De La Vega, Jordan R. Green, Ashok Samal, Balakrishnan Prabhakaran

CSE Conference and Workshop Papers

Although still in experimental stage, articulation-based silent speech interfaces may have significant potential for facilitating oral communication in persons with voice and speech problems. An articulation-based silent speech interface converts articulatory movement information to audible words. The complexity of speech production mechanism (e.g., co-articulation) makes the conversion a formidable problem. In this paper, we reported a novel, real-time algorithm for recognizing words from continuous articulatory movements. This approach differed from prior work in that (1) it focused on word-level, rather than phoneme-level; (2) online segmentation and recognition were conducted at the same time; and (3) a symbolic representation (SAX) was …

Individual Articulator's Contribution To Phoneme Production, Jun Wang, Jordan R. Green, Ashok Samal

CSE Conference and Workshop Papers

Speech sounds are the result of coordinated movements of individual articulators. Understanding each articulator’s role in speech is fundamental not only for understanding how speech is produced, but also for optimizing speech assessments and treatments. In this paper, we studied the individual contributions of six articulators, tongue tip, tongue blade, tongue body front, tongue body back, upper lip, and lower lip to phoneme classification. A total of 3,838 vowel and consonant production samples were collected from eleven native English speakers. The results of speech movement classification using a support vector machine indicated that the tongue encoded significantly more information than …

Prediction Models For Solitary Pulmonary Nodules Based On Curvelet Textural Features And Clinical Parameters, Jing-Jing Wang, Hai-Feng Wu, Tao Sun, Xia Li, Wei Wang, Li-Xin Tao, Da Huo, Ping-Xin Lv, Wen He, Xiu-Hua Guo

Research outputs 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. …

Functional Near Infrared Detection Of Real And Imagined Finger Taps Using Support Vector Machine, Linear Discriminant Analysis, And Decision Tree Classification Methods, Eugene A. Stoudenmire

Computational Modeling & Simulation Engineering Theses & Dissertations

This study investigates the thesis that given cerebral response samples of an individual's left, right, both, and imagined finger tapping, continuous wave (CW) functional Near Infrared (fNIR), unregistered with fMRI, can differentiate between any two of the four categories.

Fifty subjects were outfitted with a single source/detector attached to a single, square pad, affixed to their heads using devices such as elastic bands and caps for light shielding. Slides depicting arrows pointing left, right, both directions, or made of dashed lines were presented to each subject, with a slide of text interspersed between each. Subjects tapped with their left finger, …