Consonant-to-Vowel/Vowel-to-Consonant Transitions to Analyze the Speech of Cochlear Implant Users

Batliner

Polterauer

et al. 2022

J Speech Lang Hear Res

Purpose: The aim of this study was to investigate the speech prosody of postlingually deaf cochlear implant (CI) users compared with control speakers without hearing or speech impairment. Method: Speech recordings of 74 CI users (37 males and 37 females) and 72 age-balanced control speakers (36 males and 36 females) are considered. All participants are German native speakers and read Der Nordwind und die Sonne (The North Wind and the Sun), a standard text in pathological speech analysis and phonetic transcriptions. Automatic acoustic analysis is performed considering pitch, loudness, and duration features, including speech rate and rhythm. Results: In general, duration and rhythm features differ between CI users and control speakers. CI users read slower and have a lower voiced segment ratio compared with control speakers. A lower voiced ratio goes along with a prolongation of the voiced segments' duration in male and with a prolongation of pauses in female CI users. Rhythm features in CI users have higher variability in the duration of vowels and consonants than in control speakers. The use of bilateral CIs showed no advantages concerning speech prosody features in comparison to unilateral use of CI. Conclusions: Even after cochlear implantation and rehabilitation, the speech of postlingually deaf adults deviates from the speech of control speakers, which might be due to changed auditory feedback. We suggest considering changes in temporal aspects of speech in future rehabilitation strategies. Supplemental Material: https://doi.org/10.23641/asha.21579171

Section: Rhythm Featuresmentioning

confidence: 86%

Adult Cochlear Implant Users Versus Typical Hearing Persons: An Automatic Analysis of Acoustic–Prosodic Parameters

Batliner

Polterauer

et al. 2022

J Speech Lang Hear Res

“…People suffering from severe to profound deafness may experience different speech disorders such as decreased intelligibility and changes in terms of articulation [25]. Acoustic analysis is performed in order to detect articulatory problems in the speech of CI users by detecting the voiceless-to-voiced (Onset) and voiced-to-voiceless (Offset) transitions, which are considered to model the difficulties of the CI users to start/stop the movement of the vocal folds [26,27]. The method used to identify the transitions is based on the presence of the fundamental frequency of speech (pitch) in short-time frames as it was shown in [28].…”

Section: Automatic Detection Of Disordered Speech In CI Usersmentioning

confidence: 99%

Multi-channel spectrograms for speech processing applications using deep learning methods

Klumpp

Vásquez-Correa

et al. 2020

Pattern Anal Applic

Self Cite

Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.

“…In [2] speech intelligibility of 50 CI users is evaluated using an automatic speech recognition system and compared with 50 Healthy Controls (HC). Recently in [3] automatic classification using Support Vector Machines (SVM) between 20 CI users and 20 healthy speakers was performed in order to evaluate articulation disorders considering acoustic features. For the case of pathological speech detection, CNNs have outperformed classical machine learning methods [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Vásquez-Correa

Gollwitzer

et al. 2019

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.