Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Generalization of Deep Neural Network Acoustic Models with
  Length Perturbation and N-best Based Label Smoothing   We introduce two techniques, length perturbation and n-best based label
smoothing, to improve generalization of deep neural network (DNN) acoustic
models for automatic speech recognition (ASR). Length perturbation is a data
augmentation algorithm that randomly drops and inserts frames of an utterance
to alter the length of the speech feature sequence. N-best based label
smoothing randomly injects noise to ground truth labels during training in
order to avoid overfitting, where the noisy labels are generated from n-best
hypotheses. We evaluate these two techniques extensively on the 300-hour
Switchboard (SWB300) dataset and an in-house 500-hour Japanese (JPN500) dataset
using recurrent neural network transducer (RNNT) acoustic models for ASR. We
show that both techniques improve the generalization of RNNT models
individually and they can also be complementary. In particular, they yield good
improvements over a strong SWB300 baseline and give state-of-art performance on
SWB300 using RNNT models.

37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex