Abstract
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.
Highlights
In speech and audio processing applications, the data are commonly processed by computing compressed representations that may not capture the dynamic information of the signals
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 cochlear implant (CI) users by training a bi-class convolutional neural networks (CNNs) 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)
Summary
In speech and audio processing applications, the data are commonly processed by computing compressed representations that may not capture the dynamic information of the signals. In [11] a methodology was presented to enhance noisy audio signals using complex spectrograms and CNNs. In that work, the real and imaginary part of the STFT is computed to form a 2D-channel spectrogram, which is processed by the convolution layers; the amplitude and phase information of the signal are considered to extract the feature maps. Cochleagrams are obtained with a Gammatone filter bank, which is based on the cochlear model proposed in [13], which consists of an array of bandpass filters organized from high frequency at the base of the cochlea, to low frequencies at the apex (innermost part of the cochlea) Both Mel and Gammatone spectrograms are computed based on the STFT whose time and frequency resolutions are determined by the size of the analysis window and the time-shift.
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