--- INTERSPEECH 2014 - SINGAPORE
--- September 14-18, 2014
--- http://www.INTERSPEECH2014.org
The INTERSPEECH 2014 Organising Committee is pleased to announce
the following 8 tutorials presented by distinguished speakers
at the conference and will be offered on Sunday, 14 September 2014.
All Tutorials will be of three (3) hours duration and require
an additional registration fee (separate from the conference
registration fee).
• Non-speech acoustic event detection and classification
• Contribution of MRI to Exploring and Modeling Speech Production
• Computational Models for Audiovisual Emotion Perception
• The Art and Science of Speech Feature Engineering
• Recent Advances in Speaker Diarization
• Multimodal Speech Recognition with the AusTalk 3D
Audio-Visual Corpus
• Semantic Web and Linked Big Data Resources for
Spoken Language Processing
• Speech and Audio for Multimedia Semantics
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ISCSLP Tutorials @ INTERSPEECH 2014
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Additionally, the ISCSLP 2014 Organising Committee welcomes
the INTERSPEECH 2014 delegates to join the 4 ISCSLP tutorials
which will be offered on Saturday, 13 September 2014.
• Adaptation Techniques for Statistical Speech Recognition
• Emotion and Mental State Recognition: Features, Models, System
Applications and Beyond
• Unsupervised Speech and Language Processing via Topic Models
• Deep Learning for Speech Generation and Synthesis
More information available at:
http://www.interspeech2014.org/public.php?page=tutorial.html
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Tutorials Description
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T1: Non-speech acoustic event detection and classification
The research in audio signal processing has been dominated by
speech research, but most of the sounds in our real-life
environments are actually non-speech events such as cars passing
by, wind, warning beeps, and animal sounds. These acoustic events
contain much information about the environment and physical
events that take place in it, enabling novel application areas such
as safety, health monitoring and investigation of biodiversity.
But while recent years have seen wide-spread adoption of
applications such as speech recognition and song recognition,
generic computer audition is still in its infancy.
Non-speech acoustic events have several fundamental differences to
speech, but many of the core algorithms used by speech researchers
can be leveraged for generic audio analysis. The tutorial is a
comprehensive review of the field of acoustic event detection as it
currently stands. The goal of the tutorial is foster interest in
the community, highlight the challenges and opportunities and
provide a starting point for new researchers. We will discuss what
acoustic event detection entails, the commonalities differences
with speech processing, such as the large variation in sounds and
the possible overlap with other sounds. We will then discuss basic
experimental and algorithm design, including descriptions
of available databases and machine learning methods. We will then
discuss more advanced topics such as methods to deal with
temporally overlapping sounds and modelling the relations between
sounds. We will finish with a discussion of
avenues for future research.
Organizers: Tuomas Virtanen and Jort F. Gemmeke
T2: Contribution of MRI to Exploring and Modeling Speech Production
Magnetic resonance imaging (MRI) provides us a magic vision to look
into the human body in various ways not only with static imaging
but also with motion imaging. MRI has been a powerful technique for
speech research to study finer anatomy of the speech organs or to
visualize true vocal tracts in three dimensions. Inherent problems
of slow image acquisition for speech tasks or insufficient signal-
to-noise ratio for microscopic observation have been the cost for
researchers to search for task-specific imaging techniques.
The recent advances of the 3-Tesla technology suggest more
practical solutions to broader applications of MRI by overcoming
previous technical limitations. In this joint tutorial in two
parts, we summarize our previous effort to accumulate
scientific knowledge with MRI and to advance speech modeling
studies for future development. Part I, given by Kiyoshi Honda,
introduces how to visualize the speech organs and vocal tracts by
presenting techniques and data for finer static
imaging, synchronized motion imaging, surface marker tracking,
real-time imaging, and vocal-tract mechanical modeling. Part 2,
presented by Jianwu Dang, focuses on applications of MRI for
phonetics of Mandarin vowels, acoustics of the vocal tracts
with side branches, analysis and simulation in search of talker
characteristics, physiological modeling of the articulatory system,
and motor control paradigm for speech articulation.
Organizers: Kiyoshi HONDA and Jianwu DANG
T3: Computational Models for Audiovisual Emotion Perception
In this tutorial we will explore engineering approaches to
understanding human emotion perception, focusing both on modeling
and application. We will highlight both current and historical
trends in emotion perception modeling, focusing on
both psychological and engineering-driven theories of perception
(statistical analyses, data-driven computational modeling, and
implicit sensing). The importance of this topic can be appreciated
from both an engineering viewpoint, any system that either models
human behavior or interacts with human partners must
understand emotion perception as it fundamentally underlies and
modulates our communication, or from a psychological perspective,
emotion perception is also used in the diagnosis of many mental
health conditions and is tracked in therapeutic
interventions. Research in emotion perception seeks to identify
models that describe the felt sense of ‘typical’ emotion expression
– i.e., an observer/evaluator’s attribution
of the emotional state of the speaker. This felt sense is a
function of the methods through which individuals integrate the
presented multimodal emotional information.
We will cover psychological theories of emotion, engineering models
of emotion, and experimental approaches to measure emotion. We will
demonstrate how these modeling
strategies can be used as a component of emotion classification
frameworks and how they can be used to inform the design of
emotional behaviors.
Organizers: Emily Mower Provost and Carlos Busso
T4: The Art and Science of Speech Feature Engineering
With significant advances in mobile technology and audio sensing
devices, there is a fundamental need to describe vast amounts of
audio data in terms of well representative lower dimensional
descriptors for efficient automatic processing. The extraction of
these signal representations, also called features,
constitutes the first step in processing a speech signal. The art
and science of feature engineering relates to addressing the two
inherent challenges - extracting sufficient information from the
speech signal for the task at hand and suppressing
the unwanted redundancies for computational efficiency and
robustness. The area of speech feature extraction combines a wide
variety of disciplines like signal processing, machine learning,
psychophysics, information theory, linguistics and physiology.
It has a rich history spanning more than five decades and has seen
tremendous advances in the last few years. This has propelled the
transition of the speech technology from controlled environments to
millions of end user applications.
In this tutorial, we review the evolution of speech feature
processing methods, summarize the recent advances of the last two
decades and provide insights into the future of feature
engineering. This will include the discussions on the spectral
representation methods developed in the past, human auditory
motivated techniques for robust speech processing, data driven
unsupervised features like ivectors and recent advances in deep
neural network based techniques. With experimental results,
we will also illustrate the impact of these features for various
state-of-the-art speech processing systems. The future of speech
signal processing will need to address
various robustness issues in complex acoustic environments while
being able to derive useful information from big data.
Organizers: Sriram Ganapathy and Samuel Thomas
T5: Recent Advances in Speaker Diarization
The tutorial will start with an introduction to speaker diarization
giving a general overview of the subject. Afterwards, we will cover
the basic background including
feature extraction, and common modeling techniques such as GMMs and
HMMs. Then, we will discuss the first processing step usually done
in speaker diarization which is voice activity detection. We will
consequently describe the classic approaches
for speaker diarization which are widely used today. We will then
introduce state-of-the-art techniques in speaker recognition
required to understand modern speaker diarization techniques.
Following, we will describe approaches for speaker diarization
using advanced representation methods (supervectors, speaker
factors, i-vectors) and we will describe supervised and
unsupervised learning techniques used for speaker diarization. We
will also discuss issues such as coping with unknown number of
speakers, detecting and dealing with overlapping speech,
diarization confidence estimation, and online speaker diarization.
Finally we will discuss two recent works: exploiting a-prioiri
acoustic information (such as processing a meeting
when some of the participants are known in advanced to the system,
and training data is available for them),
The second recent work is modeling speaker-turn dynamics. If time
permits, we will also discuss concepts
such as multi-modal diarization and using TDOA (time difference of
arrival) for diarization of meetings.
Organizers: Hagai Aronowitz
T6: Multimodal Speech Recognition with the AusTalk 3D Audio-Visual
Corpus
This tutorial will provide attendees a brief overview of 3D based
AVSR research. In this tutorial, attendees will learn how to use
the newly developed 3D based audio visual data corpus we derived
from the AusTalk corpus (https://austalk.edu.au/)
for audio-visual speech/speaker recognition. In addition, we also
plan to introduce some results using this newly developed 3D audio-
visual data corpus, which show that there is a significant speech
accuracy increase by integrating both depth-level and grey-level
visual features. In the first part of the tutorial, we will review
some recent works published in the last decade, so that attendees
can obtain an overview of the fundamental concepts
and challenges in this field. In the second part of the tutorial,
we will briefly describe the recording protocol and contents of the
3D data corpus, and show attendees how to use
this corpus for their own research. In the third part of this
tutorial, we will present our results using the 3D data corpus. The
experimental results show that, compared with the
conventional AVSR based on the audio and grey-level visual
features, the integration of grey and depth visual information can
boost the AVSR accuracy significantly. Moreover,
we will also experimentally explain why adding depth information
can benefit the standard AVSR systems. Eventually, through our
tutorial, we hope we can inspire more researchers in the community
to contribute to this exciting research.
Organizers: Roberto Togneri, Mohammed Bennamoun and Chao (Luke) Sui
T7: Semantic Web and Linked Big Data Resources for Spoken Language
Processing
State-of-the-art statistical spoken language processing typically
requires significant manual effort to construct domain-specific
schemas (ontologies) as well as manual effort to annotate training
data against these schemas. At the same time, a recent surge of
activity and progress on semantic web-related
concepts from the large search-engine companies represents a
potential alternative to the manually intensive design of spoken
language processing systems. Standards such as schema.org have been
established for schemas (ontologies) that webmasters can use to
semantically and uniformly markup their web pages.
Search engines like Bing, Google, and Yandex have adopted these
standards and are leveraging them to create semantic search engines
at the scale of the web. As a result, the open linked data
resources and semantic graphs covering various domains (such as
Freebase [3]) have grown massively every year and contains far more
information than any single resource anywhere on the Web.
Furthermore, these resources contain links to text data (such as
Wikipedia pages) related to the knowledge in the graph.
Recently, several studies on speech language processing started
exploiting these massive linked data resources for language
modeling and spoken language understanding. This tutorial will
include a brief introduction to the semantic web and the linked
data structure, available resources, and querying languages.
An overview of related work on information extraction and language
processing will be presented, where the main focus will be on
methods for learning spoken language
understanding models from these resources.
Organizers: Dilek Hakkani-Tür and Larry Heck
T8: Speech and Audio for Multimedia Semantics
Internet media sharing sites and the one-click upload capability of
smartphones are producing a deluge of multimedia content. While
visual features are often dominant in such material, acoustic and
speech information in particular often complements it.
By facilitating access to large amounts of data, the text-based
Internet gave a huge boost to the field of natural language
processing. The vast amount of consumer-produced video becoming
available now will do the same for video processing, eventually
enabling semantic understanding of multimedia material, with
implications for human computer interaction, robotics, etc.
Large-scale multi-modal analysis of audio-visual material is now
central to a number of multi-site research projects around the
world. While each of these have slightly different targets, they
are facing largely the same challenges: how to robustly and
efficiently process large amounts of data, how to represent and
then fuse information across modalities, how to train classifiers
and segmenters on unlabeled data, how to include human feedback,
etc.
In this tutorial, we will present the state of the art in
large-scale video, speech, and non-speech audio processing, and
show how these approaches are being applied to tasks
such as content based video retrieval (CBVR) and multimedia event
detection (MED). We will introduce the most important tools and
techniques, and show how the combination of
information across modalities can be used to induce semantics on
multimedia material through ranking of information and fusion.
Finally, we will discuss opportunities
for research that the INTERSPEECH community specifically will find
interesting and fertile.
Organizers: Florian Metze and Koichi Shinoda
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ISCSLP Tutorials @ INTERSPEECH 2014 Description
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ISCSLP-T1: Adaptation Techniques for Statistical Speech Recognition
Adaptation is a technique to make better use of existing models for
test data from new acoustic or linguistic conditions. It is an
important and challenging research area of statistical speech
recognition. This tutorial gives a systematic
review of fundamental theories as well as introduction of state-
of-the-art adaptation techniques. It includes both acoustic and
language model adaptation. Following a simple example
of acoustic model adaptation, basic concepts, procedures and
categories of adaptation will be introduced. Then, a number of
advanced adaptation techniques will be discussed,
such as discriminative adaptation, Deep Neural Network adaptation,
adaptive training, relationship to noise robustness etc. After the
detailed review of acoustic model adaptation,
an introduction of language model adaptation, such as topic
adaptation will also be given. The whole tutorial is then
summarised and future research direction will be discussed.
Organizers: Kai Yu
ISCSLP-T2: Emotion and Mental State Recognition: Features, Models,
System Applications and Beyond
Emotion recognition is the ability to identify what you are feeling
from moment to moment and to understand the connection between your
feelings and your expressions. In today’s world, human-computer
interaction (HCI) interface undoubtedly plays an important role in
our daily life. Toward harmonious HCI interfaces, automated
analysis and recognition of human emotion has attracted increasing
attention from researchers in multidisciplinary research fields. A
specific area of current interest that also has key implications
for HCI is the estimation of cognitive load (mental workload),
research into which is still at an early stage. Technologies for
processing daily activities including speech, text and music have
expanded the interaction modalities between humans and computer-
supported communicational artifacts.
In this tutorial, we will present theoretical and practical work
offering new and broad views of the latest research in emotional
awareness from audio and speech. We discuss several parts
spanning a variety of theoretical background and applications
ranging from salient emotional features,
emotional-cognitive models, compensation methods for variability
due to speaker and linguistic content, to machine learning
approaches applicable to emotion recognition. In each topic, we
will review the state of the art by introducing current methods and
presenting several applications. In particular, the application to
cognitive load estimation will be discussed, from its
psychophysiological origins to system design considerations.
Eventually, technologies developed in different areas will be
combined for future applications, so in addition to a survey of
future research challenges, we will envision a few scenarios in
which affective computing can make a difference.
Organizers: Chung-Hsien Wu, Hsin-Min Wang, Julien Epps and
Vidhyasaharan Sethu
ISCSLP-T3: Unsupervised Speech and Language Processing via Topic Models
In this tutorial, we will present state-of-art machine learning
approaches for speech and language processing with highlight on the
unsupervised methods for structural learning from the unlabeled
sequential patterns. In general, speech and language processing
involves extensive knowledge of statistical models. We require
designing a flexible, scalable and robust system to meet
heterogeneous and non-stationary environments in the era of big
data. This tutorial starts from an introduction of unsupervised
speech and language processing based on factor analysis and
independent component analysis. The unsupervised learning is
generalized to a latent variable model which is known as the topic
model. The evolution of topic models from latent semantic analysis
to hierarchical Dirichlet process, from non-Bayesian parametric
models to Bayesian nonparametric models, and from single-layer
model to hierarchical tree model shall be surveyed in an organized
fashion. The inference approaches based on variational Bayesian and
Gibbs sampling are introduced. We will also present several
case studies on topic modeling for speech and language applications
including language model, document model, retrieval model,
segmentation model and summarization model. At last, we will point
out new trends of topic models for speech and language processing.
Organizers: Jen-Tzung Chien
ISCSLP-T4: Deep Learning for Speech Generation and Synthesis
Deep learning, which can represent high-level abstractions in data
with an architecture of multiple non-linear transformation, has
made a huge impact on automatic speech recognition (ASR)
research, products and services. However, deep learning for speech
generation and synthesis (i.e., text-to-speech), which is an
inverse process of speech recognition (i.e., speech-to-text),
has not generated the similar momentum as it is for ASR yet.
Recently, motivated by the success of Deep Neural Networks in
speech recognition, some neural network based research attempts
have been tried successfully on improving the performance of
statistical parametric based speech generation/synthesis. In this
tutorial, we focus on deep learning approaches to the problems in
speech generation and synthesis, especially on Text-to-Speech (TTS)
synthesis and voice conversion.
First, we give a review for the current main stream of statistical
parametric based speech generation and synthesis, or the GMM-HMM
based speech synthesis and GMM-based voice conversion with emphasis
on analyzing the major factors responsible for the quality problems
in the GMM-based voice synthesis/conversion and the intrinsic
limitations of a decision-tree based, contextual state
clustering and state-based statistical distribution modeling. We
then present the latest deep learning algorithms for feature
parameter trajectory generation, in contrast to deep learning for
recognition or classification. We cover common technologies in Deep
Neural Network (DNN) and improved DNN: Mixture Density Networks
(MDN), Recurrent Neural Networks (RNN) with Bidirectional Long
Short Term Memory (BLSTM) and Conditional RBM (CRBM). Finally, we
share our research insights and hand-on experience on building
speech generation and synthesis systems based upon deep learning
algorithms.
Organizers: Yao Qian and Frank K. Soong
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