Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach

Abstract

Purpose The goal of this project was to quantify the articulatory distinctiveness of eight major English vowels and eleven English consonants based on tongue and lip movement time series data using a data-driven approach. Method Tongue and lip movements of eight vowels and eleven consonants from ten healthy talkers were collected. First, classification accuracies were obtained using two complementary approaches: Procrustes analysis and 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 using multi-dimensional scaling. Results Vowel classification accuracies of 91.67% and 89.05% and consonant classification accuracies of 91.37% and 88.94% were obtained using Procrustes analysis and support vector machine, respectively. Articulatory vowel and consonant spaces were derived based on the pairwise Procrustes distances. Conclusion The articulatory vowel space derived in this study resembled the long-standing descriptive articulatory vowel space defined by tongue height and advancement. The articulatory consonant space was consistent with feature-based classification of English consonants. The derived articulatory vowel and consonant spaces may have clinical implications including serving as an objective measure of the severity of articulatory impairment.

Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization

“…Wave records tongue movements by establishing a calibrated electromagnetic field that induces electric current into tiny sensor coils that are attached to the surface of the articulators. A similar data collection procedure has been used in [22,23,38]. The spatial precision of motion tracking using Wave is approximately 0.5 mm [43].…”

“…However, speaker-independent approaches are important for reducing the amount of training data required from each user. Only limited articulatory data samples are often available from individuals with ALS (even with healthy talkers) due to the logistic difficulty of articulatory data collection [22]. For example, in data collection using electromagnetic articulograph (EMA), small sensors have to be attached on the tongue using dental glue [23].…”

Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies

“…As mentioned previously, real-time preprocessing of the positional time series was conducted, including subtraction of head movements from tongue and lip data and noise reduction using a 20 Hz low pass filter (Green et al, 2003;Wang et al, 2013a). Although the tongue and lip sensors are 5D, only the 3D spatial data (i.e., x, y, and z) were used in this experiment.…”

“…Figure 2 (left) shows the positions of the five sensors attached to a participant's forehead, tongue, and lips (Green et al, 2003;Wang et al, 2013a). One 6 DOF (spatial and rotational) head sensor was attached to a nose bridge on a pair of glasses (rather than on forehead skin directly), to avoid the skin artifact (Green et al, 2007).…”

“…Two 5 DOF sensors -TT (Tongue Tip) and TB (Tongue Body Back) -were attached on the midsagittal of the tongue. TT was located approximately 10 mm from the tongue apex (Wang et al, 2011(Wang et al, , 2013a. TB was placed as far back as possible, depending on the participant's tongue length (Wang et al, 2013b).…”