  In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic
speech recognition (ASR) system, the phoneme class conditional probabilities
are estimated by first extracting acoustic features from the speech signal
based on prior knowledge such as, speech perception or/and speech production
knowledge, and, then modeling the acoustic features with an ANN. Recent
advances in machine learning techniques, more specifically in the field of
image processing and text processing, have shown that such divide and conquer
strategy (i.e., separating feature extraction and modeling steps) may not be
necessary. Motivated from these studies, in the framework of convolutional
neural networks (CNNs), this paper investigates a novel approach, where the
input to the ANN is raw speech signal and the output is phoneme class
conditional probability estimates. On TIMIT phoneme recognition task, we study
different ANN architectures to show the benefit of CNNs and compare the
proposed approach against conventional approach where, spectral-based feature
MFCC is extracted and modeled by a multilayer perceptron. Our studies show that
the proposed approach can yield comparable or better phoneme recognition
performance when compared to the conventional approach. It indicates that CNNs
can learn features relevant for phoneme classification automatically from the
raw speech signal.
