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Full-Text Articles in Medicine and Health Sciences
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
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
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 …