Increased midsagittal tongue velocity as indication of articulatory compensation in patients with lateral partial glossectomies

Rastadmehr, Orchid; Bressmann, Tim; Smyth, Russell; Irish, Jonathan C.

doi:10.1002/hed.20772

Cited by 42 publications

(39 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This result is in agreement with the results obtained in [3], which showed that velocity was an indicator for abnormal speech, and is an encouraging one as it reflects that our approach gave results identical to a method that relied on manual segmentation. Furthermore, the mean classification accuracy of Vy-B was 9% greater than the accuracy of Vy-P, suggesting that the y-velocity exhibited in the blade region differentiated between normal and abnormal tongue motions much more effectively than those exhibited in the posterior region.…”

Section: Methodssupporting

confidence: 92%

“…The first experiment examined how our velocitybased descriptors can differentiate tongue motions produced from patients with and without speech impediments. For this experiment, we employed the data from [3], which consists of 12 studies from patients with normal speech and 12 studies from the same patients with abnormal speech (before and after undergoing lateral partial glossectomies). Each study consists of an audio recording and an US image sequence of 200-420 frames that captures the patient's tongue motion while reading a passage.…”

Section: Methodsmentioning

confidence: 99%

“…Following [3], which concluded that velocity is an indicator for normal and abnormal tongue motion, we have built our descriptors based on velocities.…”

Section: Feature Extractionmentioning

confidence: 99%

“…For analysis of tongue motions from US images, most of the previous studies [3,4,5,6] only examined tongue motions using velocity measurements that relied on manual delineation of tongue contours, which is both labourious and difficult [7]. There exist a few studies that employed machine learning algorithms, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

Tang

Hamarneh

Bressmann

2011

Machine Learning in Medical Imaging

Self Cite

View full text Add to dashboard Cite

Abstract. Analysis of tongue motions as captured in dynamic ultrasound (US) images has been an important tool in speech research. Previous studies generally required semi-automatic tongue segmentations to perform data analysis. In this paper, we adopt a machine learning approach that does not require tongue segmentation. Specifically, we employ advanced normalization procedures to temporally register the US sequences using their corresponding audio files. To explicitly encode motion, we then register the image frames spatio-temporally to compute a set of deformation fields from which we construct the velocity-based and spatiotemporal gestural descriptors, where the latter explicitly encode tongue dynamics during speech. Next, making use of the recently proposed Histogram Intersection Kernel, we perform support vector machine classification to evaluate the extracted descriptors with a set of clinical measures. We applied our method to speech abnormality and tongue gestures prediction. Overall, differentiating tongue motion, as produced by patients with or without speech impediments on a dataset of 24 US sequences, was achieved with classification accuracy of 94%. When applied to another dataset of 90 US sequences for two other classification tasks, accuracies were 86% and 84%.

show abstract

Section: Methodssupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

“…Following [3], which concluded that velocity is an indicator for normal and abnormal tongue motion, we have built our descriptors based on velocities.…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

Tang

Hamarneh

Bressmann

2011

Machine Learning in Medical Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Acoustic analysis of speech and voice has been used for a long time as tool for analysis of altered cases, for promoting more accurate diagnoses, and as the monitoring procedure of the therapeutic process, both by speech language pathologist and by the patient (1)(2)(3)(4) . The articulatory analysis in the area of speech and orofacial motricity emerges as a new possibility, especially at the national level (5)(6)(7)(8)(9)(10) , for integration of these analyses already mentioned, using, for example, the ultrasonography for evaluation of tongue movements (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) , and of the hyoid bone (19,(23)(24)(25)(26)(27) , without inserting devices within the oral cavity.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound applicability in Speech Language Pathology and Audiology

Barberena

Brasil

Melo

et al. 2014

CoDAS

View full text Add to dashboard Cite

PURPOSE: To present recent studies that used the ultrasound in the fields of Speech Language Pathology and Audiology, which evidence possibilities of the applicability of this technique in different subareas. RESEARCH STRATEGY: A bibliographic research was carried out in the PubMed database, using the keywords "ultrasonic," "speech," "phonetics," "Speech, Language and Hearing Sciences," "voice," "deglutition," and "myofunctional therapy," comprising some areas of Speech Language Pathology and Audiology Sciences. The keywords "ultrasound," "ultrasonography," "swallow," "orofacial myofunctional therapy," and "orofacial myology" were also used in the search. SELECTION CRITERIA: Studies in humans from the past 5 years were selected. In the preselection, duplicated studies, articles not fully available, and those that did not present direct relation between ultrasound and Speech Language Pathology and Audiology Sciences were discarded. DATA ANALYSIS: The data were analyzed descriptively and classified subareas of Speech Language Pathology and Audiology Sciences. The following items were considered: purposes, participants, procedures, and results. RESULTS: We selected 12 articles for ultrasound versus speech/phonetics subarea, 5 for ultrasound versus voice, 1 for ultrasound versus muscles of mastication, and 10 for ultrasound versus swallow. Studies relating "ultrasound" and "Speech Language Pathology and Audiology Sciences" in the past 5 years were not found. CONCLUSION: Different studies on the use of ultrasound in Speech Language Pathology and Audiology Sciences were found. Each of them, according to its purpose, confirms new possibilities of the use of this instrument in the several subareas, aiming at a more accurate diagnosis and new evaluative and therapeutic possibilities.

show abstract

Speech Disorders Related to Head and Neck Cancer

Bressmann¹

2021

The Handbook of Language and Speech Disorders

View full text Add to dashboard Cite

Increased midsagittal tongue velocity as indication of articulatory compensation in patients with lateral partial glossectomies

Cited by 42 publications

References 26 publications

A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

Ultrasound applicability in Speech Language Pathology and Audiology

Speech Disorders Related to Head and Neck Cancer

Contact Info

Product

Resources

About