Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards End-to-End Speech Recognition with Deep Convolutional Neural
  Networks   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.

13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th