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

Arias-Vergara, Tomás; Vásquez-Correa, Juan Camilo; Gollwitzer, Sandra; Orozco-Arroyave, Juan Rafael; Schuster, Maria; Nöth, Elmar

doi:10.1007/978-3-030-33904-3_64

Cited by 2 publications

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References 9 publications

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“…The proposed approach is then evaluated in two speech processing applications: automatic detection of disordered speech of cochlear implant (CI) users and phoneme class recognition to extract phone-attribute features. In our previous work [12], we showed that combining at least two different time-frequency representations of the signals can improve the automatic detection of speech deficits in CI users by training a bi-class CNN to differentiate between speech signals from CI users and healthy control (HC) speakers. This paper extends the use of multi-channel spectrograms to phoneme recognition using recurrent neural networks with convolutional layers (CRNN).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%