Speech differences between CI users with pre- and postlingual onset of deafness detected by speech processing methods on voiceless to voice transitions

Vergara, T Arias; Gollwitzer, Sandra; Orozco-Arroyave, JR; Vasquez-Correa, JC; Nöth, Elmar; Högerle, Catalina; Schuster, Maria

doi:10.1055/s-0039-1686328

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“…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

Arias-Vergara

Klumpp

Vásquez-Correa

et al. 2020

Pattern Anal Applic

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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.

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Section: Automatic Detection Of Disordered Speech In CI Usersmentioning

confidence: 99%