  Automatic speech recognition systems usually rely on spectral-based features,
such as MFCC of PLP. These features are extracted based on prior knowledge such
as, speech perception or/and speech production. Recently, convolutional neural
networks have been shown to be able to estimate phoneme conditional
probabilities in a completely data-driven manner, i.e. using directly temporal
raw speech signal as input. This system was shown to yield similar or better
performance than HMM/ANN based system on phoneme recognition task and on large
scale continuous speech recognition task, using less parameters. Motivated by
these studies, we investigate the use of simple linear classifier in the
CNN-based framework. Thus, the network learns linearly separable features from
raw speech. We show that such system yields similar or better performance than
MLP based system using cepstral-based features as input.
