2019 INTERSPEECH INTERSPEECH 2019

Phone-Attribute Posteriors to Evaluate the Speech of Cochlear Implant Users

Abstract

People with pre- and postlingual onset of deafness, i.e, age of occurrence of hearing loss, often present speech production problems even after hearing rehabilitation by cochlear implantation. In this paper, the speech of 20 prelinguals (aged between 18 to 71 years old), 20 postlinguals (aged between 33 to 78 years old) and 20 healthy control (aged between 31 to 62 years old) German native speakers are analyzed considering phone-attribute features extracted with pre-trained Deep Neural Networks. Speech signals are analyzed with reference to the manner of articulation of consonants according to 5 groups: nasals, sibilants, fricatives, voiced-stops, and voiceless-stops. According to the results, it is possible to detect alterations in the consonant production of CI users when compared with healthy speakers. A comprehensive evaluation of speech changes of CI users will help in the rehabilitation after deafening.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🧭 Keyword Pioneer — speech production
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Speech & Audio