End-to-End Speech Recognition From the Raw Waveform
Table 3: Comparison of models trained with or without a learnable pre-emphasis layer. All models are initialized either with the scattering or gammatone initialization, and the pooling function is a fixed squared Hanning window.
['model', 'pre-emp', 'nov93-dev LER', 'nov93-dev WER', 'nov92-eval LER', 'nov92-eval WER']
The first step in the computation of mel-filterbanks is typically the application of a pre-emphasis layer to the raw signal. Pre-emphasis is a convolution with a first-order high-pass filter of the form y[n]=x[n]−αx[n−1], with α typically equal to 0.97. This operation can be performed by a convolutional layer of kernel size 2 and stride 1, that can be plugged below time-domain filterbanks, initialized with weights [−0.971], then learned with the network. We observe a gain on both LER and WER (except on nov93-dev WER/scatt) when using pre-emphasis.