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ISCApad Archive  »  2011  »  ISCApad #154  »  Journals  »  IEEE Transactions on Audio, Speech, and Language Processing/Special Issue on Deep Learning for Speech and Language Processing

ISCApad #154

Friday, April 22, 2011 by Chris Wellekens

7-4 IEEE Transactions on Audio, Speech, and Language Processing/Special Issue on Deep Learning for Speech and Language Processing

IEEE Transactions on Audio, Speech, and Language Processing
Special Issue on Deep Learning for Speech and Language Processing

Over the past 25 years or so, speech recognition
technology has been dominated largely by hidden Markov
models (HMMs). Significant technological success has been
achieved using complex and carefully engineered variants
of HMMs. Next generation technologies require solutions to
technical challenges presented by diversified deployment
environments. These challenges arise from the many types
of variability present in the speech signal itself.
Overcoming these challenges is likely to require “deep”
architectures with efficient and effective learning

There are three main characteristics in the deep learning
paradigm: 1) layered architecture; 2) generative modeling
at the lower layer(s); and 3) unsupervised learning at the
lower layer(s) in general. For speech and language
processing and related sequential pattern recognition
applications, some attempts have been made in the past to
develop layered computational architectures that are
“deeper” than conventional HMMs, such as hierarchical HMMs,
 hierarchical point-process models, hidden dynamic models,
layered multilayer perception, tandem-architecture
neural-net feature extraction, multi-level detection-based
architectures, deep belief networks, hierarchical
conditional random field, and deep-structured conditional
random field. While positive recognition results have been
reported, there has been a conspicuous lack of systematic
learning techniques and theoretical guidance to facilitate
the development of these deep architectures. Recent
communication between machine learning researchers and
speech and language processing researchers revealed a
wealth of research results pertaining to insightful
applications of deep learning to some classical speech
recognition and language processing problems. These
results can potentially further advance the state of the
arts in speech and language processing.

In light of the sufficient research activities in this
exciting space already taken place and their importance,
we invite papers describing various aspects of deep
learning and related techniques/architectures as well as
their successful applications to speech and language
processing. Submissions must not have been previously
published, with the exception that substantial extensions
of conference or workshop papers will be considered.

The submissions must have specific connection to audio,
speech, and/or language processing. The topics of
particular interest will include, but are not limited to:

  • Generative models and discriminative statistical or neural models with deep structure
  • Supervised, semi-supervised, and unsupervised learning with deep structure
  • Representing sequential patterns in statistical or neural models
  • Robustness issues in deep learning
  • Scalability issues in deep learning
  • Optimization techniques in deep learning
  • Deep learning of relationships between the linguistic hierarchy and data-driven speech units
  • Deep learning models and techniques in applications such as (but not limited to) isolated or continuous speech recognition, phonetic recognition, music signal processing, language modeling, and language identification.

The authors are required to follow the Author’s Guide for
manuscript submission to the IEEE Transactions on Audio,
Speech, and Language Processing at

Submission deadline: September 15, 2010                 
Notification of Acceptance: March 15, 2011
Final manuscripts due: May 15, 2011
Date of publication: August 2011

For further information, please contact the guest editors:
Dong Yu (
Geoffrey Hinton (
Nelson Morgan (
Jen-Tzung Chien (
Shiegeki Sagayama (

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