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.

本文探究一种利用卷积神经网络（CNN），以原始语音信号作为输入、输出为音素类别条件概率估计的新方法，比较其在 TIMIT 音素识别任务中的表现，发现该方法可以获得与传统方法相当或更好的音素识别性能，表明 CNN 可以自动从原始语音信号中学习与音素分类相关的特征。