People with postlingual onset of deafness often present speech production problems even after hearing rehabilitation by cochlear implantation. In this paper, the speech of 20 postlingual (aged between 33 and 78 years old) and 20 healthy control (aged between 31 and 62 years old) German native speakers is analyzed considering acoustic features extracted from Consonant-to-Vowel (CV) and Vowel-to-Consonant (VC) transitions. The transitions are analyzed with reference to the manner of articulation of consonants according to 5 groups: nasals, sibilants, fricatives, voiced stops, and voiceless stops. Automatic classification between cochlear implant (CI) users and healthy speakers shows accuracies of up to 93%. Considering CV transitions, it is possible to detect specific features of altered speech of CI users. More features are to be evaluated in the future. A comprehensive evaluation of speech changes of CI users will help in the rehabilitation after deafening.

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.

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This paper proposes a methodology for automatic detection of speech disorders in Cochlear Implant users by implementing a multi-channel Convolutional Neural Network. The model is fed with a 2-channel input which consists of two spectrograms computed from the speech signals using Mel-scaled and Gammatone filter banks. Speech recordings of 107 cochlear implant users (aged between 18 and 89 years old) and 94 healthy controls (aged between 20 and 64 years old) are considered for the tests. According to the results, using 2-channel spectrograms improves the performance of the classifier for automatic detection of speech impairments in Cochlear Implant users.

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