[Elsnet-list] INTERSPEECH 2014 - Tutorials

Organization @ Interspeech 2014 organization at interspeech2014.org
Mon Jun 2 11:53:42 CEST 2014

--- 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


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:

Tutorials Description

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

     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

     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,

     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

ISCSLP Tutorials @ INTERSPEECH 2014 Description

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

     Organizers: Yao Qian and Frank K. Soong

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