  Convolutional Neural Networks (CNNs) are effective models for reducing
spectral variations and modeling spectral correlations in acoustic features for
automatic speech recognition (ASR). Hybrid speech recognition systems
incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models
(HMMs/GMMs) have achieved the state-of-the-art in various benchmarks.
Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural
Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it
feasible to train an end-to-end speech recognition system instead of hybrid
settings. However, RNNs are computationally expensive and sometimes difficult
to train. In this paper, inspired by the advantages of both CNNs and the CTC
approach, we propose an end-to-end speech framework for sequence labeling, by
combining hierarchical CNNs with CTC directly without recurrent connections. By
evaluating the approach on the TIMIT phoneme recognition task, we show that the
proposed model is not only computationally efficient, but also competitive with
the existing baseline systems. Moreover, we argue that CNNs have the capability
to model temporal correlations with appropriate context information.
