Signal Processing - May 2016 - 6

reader's choice

Top Downloads in IEEE Xplore

T

he "Reader's Choice" column
focuses on a different publication of
the IEEE Signal Processing Society.
This month we are highlighting articles
from IEEE/ACM Transactions on
Audio, Speech, and Language Processing, which covers audio, speech and
language processing and the sciences
that support them. The "word cloud"
image in this column shows the concentration of topics used in the titles of the
most downloaded papers over the past
year weighted by the number of months
the article was a top download. The
three broad topics of the title of this
journal are expanded as transducers,
room acoustics, active sound control,
human audition, analysis/synthesis/coding of music, and consumer audio;
speech analysis, synthesis, coding,
speech recognition, speaker recognition,
speech production, and perception and
speech enhancement; and speech understanding, spoken language dialog systems, translation, summarization and
document retrieval, as well as general
language modeling.
This issue's "Reader's Choice" column lists the top ten papers most
downloaded for the past year at the
time of the print deadline. Download
statistics can be found in the supplementary document on the SigPort
repository (http://sigport.org/),where
we have included inset graphs to show

Digital Object Identifier 10.1109/MSP.2016.2530629
Date of publication: 27 April 2016

6

the downloads for each month of the
previous year and show if the article is
a steady performer, a brilliant flash, a
past glory, or a rising star. Your suggestions and comments are welcome and
should be sent to Associate Editor
Michael Gormish (gormish@ieee.org).

Automatic Expressive Opinion
Sentence Generation for Enjoyable
Conversational Systems
Matsuyama, Y.; Saito, A.; Fujie, S.;
Kobayashi, T.
Rather than returning the minimum
required information in a conversation,
the authors generate sentences with
automatically gathered opinions and
IEEE Signal Processing Magazine

|

May 2016

|

novel means of expression. This sentence generation is tested in conversations between robots and humans and
is found to promote human interest.
February 2015

Convolutional Neural Networks
for Speech Recognition
Abdel-Hamid, O.; Mohamed, A.-R.;
Jiang, H.; Deng, L.; Penn, G.; Yu, D.
This paper improves on hybrid deep
neural network-hidden Markov model
with a convolutional neural network
(CNN). Features of CNNs including
weight sharing and pooling provide
invariance to speech sifts along the
frequency axis. Up to a 10% error



http://www.sigport.org/),where

Table of Contents for the Digital Edition of Signal Processing - May 2016

Signal Processing - May 2016 - Cover1
Signal Processing - May 2016 - Cover2
Signal Processing - May 2016 - 1
Signal Processing - May 2016 - 2
Signal Processing - May 2016 - 3
Signal Processing - May 2016 - 4
Signal Processing - May 2016 - 5
Signal Processing - May 2016 - 6
Signal Processing - May 2016 - 7
Signal Processing - May 2016 - 8
Signal Processing - May 2016 - 9
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Signal Processing - May 2016 - 11
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Signal Processing - May 2016 - 20
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Signal Processing - May 2016 - 27
Signal Processing - May 2016 - 28
Signal Processing - May 2016 - 29
Signal Processing - May 2016 - 30
Signal Processing - May 2016 - 31
Signal Processing - May 2016 - 32
Signal Processing - May 2016 - 33
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Signal Processing - May 2016 - 101
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Signal Processing - May 2016 - 118
Signal Processing - May 2016 - 119
Signal Processing - May 2016 - 120
Signal Processing - May 2016 - Cover3
Signal Processing - May 2016 - Cover4
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