Objectives/Hypothesis This study aimed to evaluate the long‐term swallowing performance following transoral robotic surgery (TORS) to the base of tongue (BOT) in the treatment of obstructive sleep apnea (OSA). Study Design Retrospective and prospective cohort study. Methods Data analysis of 39 patients who underwent BOT reduction via TORS to treat OSA at our center from September 2013 to April 2016. Long‐term swallowing functions were assessed using subjective self‐evaluated swallowing disturbances questionnaire (SDQ) and objective fiberoptic endoscopic evaluation of swallowing (FEES). Results Seven patients underwent TORS BOT reduction alone, whereas 32 had also uvulopalatoplasty ± tonsillectomy, with a surgical success rate of 71.4%. Mean time for swallowing evaluation was 27.4 ± 9.43 months. Twenty‐five patients completed the SDQ with an average score of 9.26 ± 10.05. In 32%, the SDQ was positive for dysphagia. In 10 out of 14 patients who underwent FEES, swallowing problems were noticed. The most common pathological findings were food residue in the vallecula followed by early spillage of food into the hypopharynx, penetration of solid food and liquid on the vocal folds surface, and aspiration. Conclusions BOT reduction via TORS has a negative effect on long‐term swallowing function. A self‐assessment questionnaire can help detect patients who suffer from swallowing impairment. Postoperative objective swallowing tests are essential not only in the immediate postoperative period but also during late routine follow‐up. Proper patient selection and detailed information about surgery and possible late‐swallowing effect are important factors before scheduling BOT reduction via TORS for OSA treatment. Level of Evidence 4 Laryngoscope, 129:422–428, 2019
Learning a new language involves constantly comparing speech productions with reference productions from the environment. Early in speech acquisition, children make articulatory adjustments to match their caregivers' speech. Grownup learners of a language tweak their speech to match the tutor reference. This paper proposes a method to synthetically generate correct pronunciation feedback given incorrect production. Furthermore, our aim is to generate the corrected production while maintaining the speaker's original voice.The system prompts the user to pronounce a phrase. The speech is recorded, and the samples associated with the inaccurate phoneme are masked with zeros. This waveform serves as an input to a speech generator, implemented as a deep learning inpainting system with a U-net architecture, and trained to output a reconstructed speech. The training set is composed of unimpaired proper speech examples, and the generator is trained to reconstruct the original proper speech. We evaluated the performance of our system on phoneme replacement of minimal pair words of English as well as on children with pronunciation disorders. Results suggest that human listeners slightly prefer our generated speech over a smoothed replacement of the inaccurate phoneme with a production of a different speaker.
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